Castillo-Fernandez et al 2017
Mary
8/3/2020
##Loading packages
suppressPackageStartupMessages({
library("R.utils")
library("missMethyl")
library("limma")
library("topconfects")
library("minfi")
library("IlluminaHumanMethylation450kmanifest")
library("MethylToSNP")
library("RColorBrewer")
library("IlluminaHumanMethylation450kanno.ilmn12.hg19")
library("eulerr")
library("plyr")
library("gplots")
library("reshape2")
library("beeswarm")
library("BSgenome")
library("MEDIPS")
library("DESeq2")
library("BSgenome.Hsapiens.UCSC.hg19")
library("ensembldb")
library("EnsDb.Hsapiens.v75")
library("GenomicRanges")
})
#customised beeswarm chart for MEDIP Seq data
make_beeswarm2<- function(dm,name,mx,groups,n=15) {
par(mar=c(3,3,1,1))
NCOLS=5
NROWS=floor(n/NCOLS)
if (n %% NCOLS > 0) { NROWS <- NROWS + 1 }
par(mfrow=c(NROWS, NCOLS))
topgenes <- rownames(head(dm[order(dm$pvalue),],n))
mx<-mx/colSums(mx)*1e6
ss <- mx[which(rownames(mx) %in% topgenes),]
n <- 1:n
g1name=levels(groups)[1]
g2name=levels(groups)[2]
g1dat <- ss[n,which(groups == g1name)]
g2dat <- ss[n,which(groups == g2name)]
g1l <-lapply(split(g1dat, row.names(g1dat)), unlist)
g2l <-lapply(split(g2dat, row.names(g2dat)), unlist)
for (i in n) {
mydat <- list(g1l[[i]],g2l[[i]])
beeswarm(mydat,cex=0.4, pch=19,
las=2, cex.lab=0.6, main=names( g1l )[i] ,
ylab="",labels = c(g1name,g2name))
grid()
}
}
#customised beeswarm chart(confects) for MEDIP Seq data
make_beeswarms_confects2 <- function(confects,name,mx,groups,n=15) {
par(mar=c(3,3,1,1))
NCOLS=5
NROWS=floor(n/NCOLS)
if (n %% NCOLS > 0) { NROWS <- NROWS + 1 }
par(mfrow=c(NROWS, NCOLS))
topgenes <- head(confects$table,n)$name
mx<-mx/colSums(mx)*1e6
ss <- mx[which(rownames(mx) %in% topgenes),]
n <- 1:n
g1name=levels(groups)[1]
g2name=levels(groups)[2]
g1dat <- ss[n,which(groups == g1name)]
g2dat <- ss[n,which(groups == g2name)]
g1l <-lapply(split(g1dat, row.names(g1dat)), unlist)
g2l <-lapply(split(g2dat, row.names(g2dat)), unlist)
for (i in n) {
mydat <- list(g1l[[i]],g2l[[i]])
beeswarm(mydat,cex=0.4, pch=19,
las=2, cex.lab=0.6, main=names( g1l )[i] ,
ylab="",labels = c(g1name,g2name))
grid()
}
}
#Annotation
names(listTables(EnsDb.Hsapiens.v75))## [1] "gene" "tx" "tx2exon" "exon"
## [5] "chromosome" "protein" "uniprot" "protein_domain"
## [9] "entrezgene" "metadata"ensgenes<-genes(EnsDb.Hsapiens.v75)
enstranscripts<-transcripts(EnsDb.Hsapiens.v75)
enspromoters<-promoters(EnsDb.Hsapiens.v75,columns=c("gene_id","gene_name"))
head(enspromoters)## GRanges object with 6 ranges and 3 metadata columns:
## seqnames ranges strand | gene_id gene_name
## <Rle> <IRanges> <Rle> | <character> <character>
## ENST00000456328 1 9869-12068 + | ENSG00000223972 DDX11L1
## ENST00000515242 1 9872-12071 + | ENSG00000223972 DDX11L1
## ENST00000518655 1 9874-12073 + | ENSG00000223972 DDX11L1
## ENST00000450305 1 10010-12209 + | ENSG00000223972 DDX11L1
## ENST00000438504 1 29171-31370 - | ENSG00000227232 WASH7P
## ENST00000541675 1 24687-26886 - | ENSG00000227232 WASH7P
## tx_id
## <character>
## ENST00000456328 ENST00000456328
## ENST00000515242 ENST00000515242
## ENST00000518655 ENST00000518655
## ENST00000450305 ENST00000450305
## ENST00000438504 ENST00000438504
## ENST00000541675 ENST00000541675
## -------
## seqinfo: 273 sequences from GRCh37 genomehead(ensgenes)## GRanges object with 6 ranges and 6 metadata columns:
## seqnames ranges strand | gene_id gene_name
## <Rle> <IRanges> <Rle> | <character> <character>
## ENSG00000223972 1 11869-14412 + | ENSG00000223972 DDX11L1
## ENSG00000227232 1 14363-29806 - | ENSG00000227232 WASH7P
## ENSG00000243485 1 29554-31109 + | ENSG00000243485 MIR1302-10
## ENSG00000237613 1 34554-36081 - | ENSG00000237613 FAM138A
## ENSG00000268020 1 52473-54936 + | ENSG00000268020 OR4G4P
## ENSG00000240361 1 62948-63887 + | ENSG00000240361 OR4G11P
## gene_biotype seq_coord_system symbol
## <character> <character> <character>
## ENSG00000223972 pseudogene chromosome DDX11L1
## ENSG00000227232 pseudogene chromosome WASH7P
## ENSG00000243485 lincRNA chromosome MIR1302-10
## ENSG00000237613 lincRNA chromosome FAM138A
## ENSG00000268020 pseudogene chromosome OR4G4P
## ENSG00000240361 pseudogene chromosome OR4G11P
## entrezid
## <list>
## ENSG00000223972 100287596,100287102
## ENSG00000227232 100287171,653635
## ENSG00000243485 100422919,100422834,100422831,...
## ENSG00000237613 654835,645520,641702
## ENSG00000268020 NA
## ENSG00000240361 NA
## -------
## seqinfo: 273 sequences from GRCh37 genomecounts
counts<-read.table("~/mr.edgeR.c.test.tsv.gz",sep="\t",header=TRUE,row.names=1)
mycol<-max(grep(".bam.counts",colnames(counts)))
counts<-counts[,1:mycol]
rownames(counts)<-paste(counts$chr,counts$start, counts$stop)
counts[,1:4]=NULL
colnames(counts)<-gsub(".bam.counts","",colnames(counts))metadata
sf1<-read.table("~/castillo_metadata/EGAD00001003159_metadata/delimited_maps/Sample_File.map",stringsAsFactors = FALSE)
rs1<-read.table("~/castillo_metadata/EGAD00001003159_metadata/delimited_maps/Run_Sample_meta_info.map",sep=";",stringsAsFactors = FALSE)
rs1[,ncol(rs1)]=NULL
md1<-apply(rs1,2,function(x){
sapply(strsplit(x,"="),"[[",2)
})
headers<-t(rs1[1,])
colnames(md1)<-sapply(strsplit(headers[,1],"="), "[[",1)
rownames(md1)<-sf1[,1]
md1<-as.data.frame(md1)
sf2<-read.table("~/castillo_metadata/EGAD00001003158_metadata/delimited_maps/Sample_File.map",stringsAsFactors = FALSE)
rs2<-read.table("~/castillo_metadata/EGAD00001003158_metadata/delimited_maps/Run_Sample_meta_info.map",sep=";",stringsAsFactors = FALSE)
rs2[,ncol(rs2)]=NULL
md2<-apply(rs2,2,function(x){
sapply(strsplit(x,"="),"[[",2)
})
headers<-t(rs2[1,])
colnames(md2)<-sapply(strsplit(headers[,1],"="), "[[",1)
rownames(md2)<-sf2[,1]
md2<-as.data.frame(md2)
md <- as.data.frame(rbind(md1,md2))
mdc <- md[grep("C",rownames(md)),]
mdw <- md[grep("W",rownames(md)),]
## CBMC
## number of natural conception 43
nrow(subset(mdc,medical_help_to_conceive=="no"))## [1] 43## number of ovarian stimulation is 36
nrow(subset(mdc,ovarian_stimulation=="yes"))## [1] 36## number of gamete intrafallopian transfer is 2
nrow(subset(mdc,gamete_intrafallopian_transfer=="yes"))## [1] 2## numberof intracytoplasmic sperm injection is 21
nrow(subset(mdc,intracytoplasmic_sperm_injection=="yes"))## [1] 21## number of ICSI frozen embryo is 9
nrow(subset(mdc,intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))## [1] 9## number of ICSI fresh embryo is 12
nrow(subset(mdc,intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))## [1] 12## WCB
## number of medical help to conceive is 54
nrow(subset(mdw,medical_help_to_conceive=="no"))## [1] 54## number of ovarian stimulation is 36
nrow(subset(mdw,ovarian_stimulation=="yes"))## [1] 36## number of gamete intrafallopian transfer is 2
nrow(subset(mdw,gamete_intrafallopian_transfer=="yes"))## [1] 2## numberof intracytoplasmic sperm injection is 18
nrow(subset(mdw,intracytoplasmic_sperm_injection=="yes"))## [1] 18## number of ICSI frozen embryo is 6
nrow(subset(mdw,intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))## [1] 6## number of ICSI fresh embryo is 12
nrow(subset(mdw,intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))## [1] 12based on the numbers, following are the contrasts
CBMC and WMBC
Natural Vs Ovarian stimulation
Natural Vs GIFT
Natural Vs ICSI fresh
Natural Vs ICSI frozen
ICSI fresh Vs frozen
Ovarian stimulation Vs GIFT
Ovarian stimulation Vs ICSI fresh
Ovarian stimulation Vs ICSI frozen
GIFT Vs ICSI fresh
GIFT Vs ICSI frozen
CBMC: Natural Vs Ovarian stimulation
NAME = "CBMC_natural_vs_ovarian_stimulation"
samplesheet<-subset(mdc, medical_help_to_conceive=="no" | ovarian_stimulation=="yes")
samplesheet$groups <- factor(samplesheet$ovarian_stimulation,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dim(y)## [1] 1449 79dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr9 128456101 128456200 9.821874 0.6481876 0.1300329 4.984798
## chr9 128456001 128456100 12.714885 0.5981254 0.1306666 4.577493
## chrM 8001 8100 55.563106 0.6261791 0.1385588 4.519232
## chr8 109249601 109249700 10.118409 0.5219290 0.1174502 4.443831
## chr3 30615401 30615500 15.888654 0.4223159 0.0953818 4.427636
## chrM 3201 3300 62.713180 0.5534153 0.1252776 4.417510
## chrM 15901 16000 80.205776 0.6469908 0.1465164 4.415826
## chr2 209767401 209767500 10.726289 0.5365324 0.1215893 4.412662
## chr1 54833001 54833100 11.211590 0.4753991 0.1080931 4.398054
## chrM 15801 15900 84.510736 0.6298150 0.1436210 4.385256
## pvalue padj
## chr9 128456101 128456200 6.202647e-07 0.0008987636
## chr9 128456001 128456100 4.705822e-06 0.0011136337
## chrM 8001 8100 6.206443e-06 0.0011136337
## chr8 109249601 109249700 8.837092e-06 0.0011136337
## chr3 30615401 30615500 9.527139e-06 0.0011136337
## chrM 3201 3300 9.984425e-06 0.0011136337
## chrM 15901 16000 1.006251e-05 0.0011136337
## chr2 209767401 209767500 1.021073e-05 0.0011136337
## chr1 54833001 54833100 1.092260e-05 0.0011136337
## chrM 15801 15900 1.158492e-05 0.0011136337confects <- deseq2_confects(res)
head(confects$table)## rank index confect effect baseMean name filtered
## 1 1 1 0.11 0.6481876 9.821874 chr9 128456101 128456200 FALSE
## 2 2 5 0.11 0.5981254 12.714885 chr9 128456001 128456100 FALSE
## 3 3 17 0.11 0.6261791 55.563106 chrM 8001 8100 FALSE
## 4 4 63 0.11 0.6469908 80.205776 chrM 15901 16000 FALSE
## 5 5 64 0.11 0.6298150 84.510736 chrM 15801 15900 FALSE
## 6 6 11 0.11 0.6080177 31.682753 chrM 16501 16600 FALSE# optional
sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
# smear plot optional
plot(log2(dge$baseMean),dge$log2FoldChange,cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 base mean",
ylim=c(-3,3),ylab="log2 fold change"
,pch=19,col="#838383")
points(log2(sig$baseMean),sig$log2FoldChange,cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","os",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm1",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm1",mx=y,groups=mygroups,n=15)
# Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': M, Un_gl000211, Un_gl000214, Un_gl000219, Un_gl000237, Un_gl000224, Un_gl000220
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 436 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 54801 | 0
## [2] 2 54801 | 0
## [3] 3 52370 | 0
## [4] 4 36552 | 32493
## [5] 5 32158 | 17770
## ... ... ... . ...
## [432] 432 16665 | 1264
## [433] 433 45162 | 0
## [434] 434 44871 | 98653
## [435] 435 46046 | 764
## [436] 436 34978 | 0
## -------
## queryLength: 436 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 436 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 9 128456101-128456200 * | MAPKAP1 0
## [2] 9 128456001-128456100 * | MAPKAP1 0
## [3] 8 109249601-109249700 * | EIF3E 0
## [4] 3 30615401-30615500 * | TGFBR2 32493
## [5] 2 209767401-209767500 * | RNA5SP117 17770
## ... ... ... ... . ... ...
## [432] 14 95551201-95551300 * | LRG_492 1264
## [433] 6 26385601-26385700 * | BTN2A2 0
## [434] 6 9025701-9025800 * | RP11-354I10.1 98653
## [435] 6 52056201-52056300 * | IL17A 764
## [436] 22 18688601-18688700 * | AC008079.9 0
## -------
## seqinfo: 29 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000216, Un_gl000231, Un_gl000224, Un_gl000225, Un_gl000214, 7_gl000195_random, Un_gl000240, Un_gl000241
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 833 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 18432 | 0
## [2] 2 17062 | 2727
## [3] 3 5610 | 0
## [4] 4 7641 | 0
## [5] 5 18432 | 0
## ... ... ... . ...
## [829] 829 63088 | 19129
## [830] 830 61918 | 0
## [831] 831 62256 | 0
## [832] 832 24800 | 0
## [833] 833 61906 | 959
## -------
## queryLength: 833 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 833 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 15 65461501-65461600 * | CLPX 0
## [2] 14 106061201-106061300 * | AL928742.12 2727
## [3] 10 12404601-12404700 * | CAMK1D 0
## [4] 10 134511601-134511700 * | INPP5A 0
## [5] 15 65461401-65461500 * | CLPX 0
## ... ... ... ... . ... ...
## [829] X 130983801-130983900 * | RP11-453F18__B.1 19129
## [830] X 49364801-49364900 * | GAGE1 0
## [831] X 70608001-70608100 * | TAF1 0
## [832] 18 7725801-7725900 * | PTPRM 0
## [833] X 49206101-49206200 * | GAGE2D 959
## -------
## seqinfo: 31 sequences from an unspecified genome; no seqlengthsNatural Vs GIFT
NAME = "CBMC_natural_vs_gamete_intrafallopian_transfer"
samplesheet<-subset(mdc, medical_help_to_conceive=="no" | gamete_intrafallopian_transfer=="yes")
samplesheet$groups <- factor(samplesheet$gamete_intrafallopian_transfer,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr5 771901 772000 11.93588 1.2545816 0.3214614 3.902744
## chr17 57900901 57901000 10.19571 1.1338602 0.2967472 3.820964
## chrUn_gl000219 48201 48300 11.56128 1.3342415 0.3627180 3.678454
## chr4 114135401 114135500 13.02012 1.1889266 0.3274092 3.631317
## chr16 83260601 83260700 11.15672 1.2051794 0.3588543 3.358408
## chr16 83260701 83260800 10.35048 1.2704975 0.3791002 3.351350
## chr1 16963701 16963800 12.38379 0.8940748 0.2668147 3.350921
## chr17 1022101 1022200 15.26086 -2.0382549 0.6275773 -3.247815
## chr9 35059701 35059800 10.43422 1.0306126 0.3224758 3.195938
## chr2 87927401 87927500 10.81617 0.9131288 0.2914849 3.132679
## pvalue padj
## chr5 771901 772000 0.0000951084 0.1238252
## chr17 57900901 57901000 0.0001329310 0.1238252
## chrUn_gl000219 48201 48300 0.0002346517 0.1313317
## chr4 114135401 114135500 0.0002819789 0.1313317
## chr16 83260601 83260700 0.0007839268 0.2143605
## chr16 83260701 83260800 0.0008041852 0.2143605
## chr1 16963701 16963800 0.0008054339 0.2143605
## chr17 1022101 1022200 0.0011629492 0.2708218
## chr9 35059701 35059800 0.0013937693 0.2885103
## chr2 87927401 87927500 0.0017321865 0.2947524sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","gift",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm2",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm2",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000219, Un_gl000237
## - in 'y': 20, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 61 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 41851 | 0
## [2] 2 23902 | 0
## [3] 3 40875 | 0
## [4] 4 21486 | 0
## [5] 5 21486 | 0
## ... ... ... . ...
## [57] 57 17655 | 18859
## [58] 58 47137 | 18957
## [59] 59 213 | 28578
## [60] 60 36987 | 2847
## [61] 61 39023 | 104693
## -------
## queryLength: 61 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 61 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 5 771901-772000 * | ZDHHC11 0
## [2] 17 57900901-57901000 * | VMP1 0
## [3] 4 114135401-114135500 * | AC004057.1 0
## [4] 16 83260601-83260700 * | CDH13 0
## [5] 16 83260701-83260800 * | CDH13 0
## ... ... ... ... . ... ...
## [57] 15 31541901-31542000 * | RP11-16E12.2 18859
## [58] 6 138464001-138464100 * | KIAA1244 18957
## [59] 1 4043901-4044000 * | RP13-614K11.1 28578
## [60] 3 49501601-49501700 * | RNA5SP130 2847
## [61] 3 187281901-187282000 * | SST 104693
## -------
## seqinfo: 23 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)
ol_down## Hits object with 40 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 21747 | 0
## [2] 2 62108 | 0
## [3] 3 41389 | 0
## [4] 4 41389 | 0
## [5] 5 18415 | 0
## ... ... ... . ...
## [36] 36 17359 | 3720
## [37] 37 62108 | 0
## [38] 38 21291 | 246
## [39] 39 62108 | 0
## [40] 40 62175 | 280694
## -------
## queryLength: 40 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 40 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 17 1022101-1022200 * | ABR 0
## [2] X 56795901-56796000 * | RP11-622K12.1 0
## [3] 4 156630301-156630400 * | GUCY1A3 0
## [4] 4 156630401-156630500 * | GUCY1A3 0
## [5] 15 65006301-65006400 * | AC100830.3 0
## ... ... ... ... . ... ...
## [36] 15 22517001-22517100 * | MIR1268A 3720
## [37] X 56796901-56797000 * | RP11-622K12.1 0
## [38] 16 72059201-72059300 * | DHODH 246
## [39] X 56796501-56796600 * | RP11-622K12.1 0
## [40] X 66176901-66177000 * | RNU6-394P 280694
## -------
## seqinfo: 12 sequences from an unspecified genome; no seqlengthsNatural Vs ICSI fresh
NAME = "CBMC_natural_vs_intracytoplasmic_sperm_injection_fresh_embryo"
samplesheet<-subset(mdc, medical_help_to_conceive=="no" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
head(samplesheet)## 260.280_ratio birth_weight dna_concentration frozen_embryo
## C1223 1.83 2645 406.78 no
## C1229 1.73 2644 677.27 no
## C1331 1.71 2510 232.52 no
## C1332 1.7 1679 275.03 no
## C1333 1.74 2425 245.98 no
## C1334 1.81 3155 427.31 no
## gamete_intrafallopian_transfer gender intracytoplasmic_sperm_injection
## C1223 no female no
## C1229 no female no
## C1331 no female no
## C1332 no female no
## C1333 no female no
## C1334 no male no
## maternal_age maternal_smoking medical_help_to_conceive mother_id
## C1223 29.14994 no no 3015
## C1229 37.33339 yes no 1033
## C1331 37.33339 yes no 1033
## C1332 34.04522 no no 1035
## C1333 34.04522 no no 1035
## C1334 25.14446 yes no 1049
## ovarian_stimulation phenotype subject_id twin_no zygosity ENA-CHECKLIST
## C1223 no non-IVF 30151 1 MZ ERC000026
## C1229 no non-IVF 10331 1 DZ ERC000026
## C1331 no non-IVF 10332 2 DZ ERC000026
## C1332 no non-IVF 10351 1 MZ ERC000026
## C1333 no non-IVF 10352 2 MZ ERC000026
## C1334 no non-IVF 10491 1 DZ ERC000026
## groups sex
## C1223 no female
## C1229 no female
## C1331 no female
## C1332 no female
## C1333 no female
## C1334 no male# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 1 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat pvalue
## chrM 15901 16000 80.40743 1.0701990 0.1949769 5.488850 4.045582e-08
## chrM 15801 15900 84.83878 1.0395112 0.1928096 5.391386 6.991612e-08
## chrM 15701 15800 129.75449 0.8665500 0.1675253 5.172652 2.307942e-07
## chrM 9801 9900 33.63418 1.0435245 0.2049771 5.090932 3.563077e-07
## chrM 16001 16100 122.12572 0.8376368 0.1646069 5.088710 3.605076e-07
## chrM 9701 9800 35.70170 1.0274973 0.2027455 5.067916 4.021954e-07
## chrM 12701 12800 79.06658 0.8565974 0.1693641 5.057728 4.242804e-07
## chrM 8101 8200 58.74518 0.9484985 0.1897191 4.999489 5.748245e-07
## chrM 12601 12700 115.37154 0.7766378 0.1555086 4.994178 5.908674e-07
## chrM 10701 10800 217.62689 0.6416102 0.1309418 4.899966 9.585312e-07
## padj
## chrM 15901 16000 5.764584e-05
## chrM 15801 15900 5.764584e-05
## chrM 15701 15800 9.994833e-05
## chrM 9801 9900 9.994833e-05
## chrM 16001 16100 9.994833e-05
## chrM 9701 9800 9.994833e-05
## chrM 12701 12800 9.994833e-05
## chrM 8101 8200 1.082600e-04
## chrM 12601 12700 1.082600e-04
## chrM 10701 10800 1.203783e-04sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","icsi",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm3",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm3",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': M, Un_gl000214, Un_gl000211, Un_gl000237
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 322 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 30885 | 0
## [2] 2 47137 | 18957
## [3] 3 40 | 0
## [4] 4 15971 | 0
## [5] 5 19852 | 0
## ... ... ... . ...
## [318] 318 39133 | 0
## [319] 319 50554 | 104525
## [320] 320 54801 | 0
## [321] 321 5958 | 0
## [322] 322 11027 | 0
## -------
## queryLength: 322 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 322 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 2 125398601-125398700 * | CNTNAP5 0
## [2] 6 138464001-138464100 * | KIAA1244 18957
## [3] 1 569601-569700 * | MTATP6P1 0
## [4] 14 58614001-58614100 * | C14orf37 0
## [5] 16 12452101-12452200 * | SNX29 0
## ... ... ... ... . ... ...
## [318] 3 194851801-194851900 * | XXYLT1 0
## [319] 8 2218001-2218100 * | MYOM2 104525
## [320] 9 128455901-128456000 * | MAPKAP1 0
## [321] 10 33599801-33599900 * | NRP1 0
## [322] 12 3496101-3496200 * | PRMT8 0
## -------
## seqinfo: 26 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000225
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 167 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 39965 | 28168
## [2] 2 54801 | 5887
## [3] 3 14995 | 0
## [4] 4 49494 | 0
## [5] 5 38772 | 0
## ... ... ... . ...
## [163] 163 62855 | 34917
## [164] 164 315 | 11633
## [165] 165 52699 | 206599
## [166] 166 7626 | 154414
## [167] 167 17368 | 0
## -------
## queryLength: 167 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 167 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 4 49277101-49277200 * | RP11-1281K21.7 28168
## [2] 9 128475401-128475500 * | MAPKAP1 5887
## [3] 13 114771701-114771800 * | RASA3 0
## [4] 7 102439401-102439500 * | FAM185A 0
## [5] 3 175318401-175318500 * | NAALADL2 0
## ... ... ... ... . ... ...
## [163] X 114994301-114994400 * | RP1-241P17.1 34917
## [164] 1 9283101-9283200 * | H6PD 11633
## [165] 8 138324301-138324400 * | RNU6-144P 206599
## [166] 10 133335001-133335100 * | AL450307.2 154414
## [167] 15 22738801-22738900 * | GOLGA6L1 0
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengthsNatural Vs ICSI frozen
NAME = "CBMC_natural_vs_intracytoplasmic_sperm_injection_frozen_embryo"
samplesheet<-subset(mdc, medical_help_to_conceive=="no" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr9 42004901 42005000 11.62129 0.8150068 0.1522669 5.352489
## chr16 21582801 21582900 12.16087 0.7208933 0.1582875 4.554328
## chr2 238790601 238790700 11.38491 0.7958142 0.1793087 4.438235
## chr9 42004801 42004900 18.32877 0.6162550 0.1403154 4.391926
## chr14 70813401 70813500 13.93291 0.6546633 0.1507850 4.341702
## chr10 86611901 86612000 11.73915 0.6847153 0.1577329 4.340978
## chr21 11096201 11096300 16.00287 0.6773271 0.1563348 4.332542
## chr15 33227601 33227700 10.82165 0.6938120 0.1618929 4.285625
## chr16 21582901 21583000 11.35963 0.6882666 0.1627079 4.230074
## chr7 57832601 57832700 15.72667 0.5589624 0.1350924 4.137629
## pvalue padj
## chr9 42004901 42005000 8.675278e-08 0.0001448771
## chr16 21582801 21582900 5.255325e-06 0.0035164779
## chr2 238790601 238790700 9.069965e-06 0.0035164779
## chr9 42004801 42004900 1.123509e-05 0.0035164779
## chr14 70813401 70813500 1.413833e-05 0.0035164779
## chr10 86611901 86612000 1.418496e-05 0.0035164779
## chr21 11096201 11096300 1.473973e-05 0.0035164779
## chr15 33227601 33227700 1.822264e-05 0.0038039760
## chr16 21582901 21583000 2.336145e-05 0.0043348461
## chr7 57832601 57832700 3.509136e-05 0.0058258879sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","icsi.fz",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm4",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm4",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': M, Un_gl000237, Un_gl000211, Un_gl000224
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 298 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 53616 | 0
## [2] 2 20069 | 16047
## [3] 3 32662 | 0
## [4] 4 53616 | 0
## [5] 5 16222 | 0
## ... ... ... . ...
## [294] 294 34598 | 0
## [295] 295 3861 | 0
## [296] 296 39153 | 0
## [297] 297 41687 | 23046
## [298] 298 5884 | 0
## -------
## queryLength: 298 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 298 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 9 42004901-42005000 * | RP11-204M4.2 0
## [2] 16 21582801-21582900 * | SCARNA6 16047
## [3] 2 238790601-238790700 * | RAMP1 0
## [4] 9 42004801-42004900 * | RP11-204M4.2 0
## [5] 14 70813401-70813500 * | SYNJ2BP-COX16 0
## ... ... ... ... . ... ...
## [294] 21 38990401-38990500 * | KCNJ6 0
## [295] 1 169538301-169538400 * | F5 0
## [296] 3 195425401-195425500 * | LINC00969 0
## [297] 4 184526501-184526600 * | snoU13 23046
## [298] 10 29797801-29797900 * | SVIL 0
## -------
## seqinfo: 26 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000224, Un_gl000231
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 135 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 17363 | 3147
## [2] 2 21448 | 31397
## [3] 3 14995 | 0
## [4] 4 38578 | 0
## [5] 5 12222 | 0
## ... ... ... . ...
## [131] 131 20192 | 0
## [132] 132 14956 | 0
## [133] 133 28820 | 56421
## [134] 134 1131 | 0
## [135] 135 61537 | 0
## -------
## queryLength: 135 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 135 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 15 22671901-22672000 * | MIR4509-1 3147
## [2] 16 80978201-80978300 * | CMC2 31397
## [3] 13 114794601-114794700 * | RASA3 0
## [4] 3 158818901-158819000 * | IQCJ 0
## [5] 12 54883001-54883100 * | RP11-753H16.3 0
## ... ... ... ... . ... ...
## [131] 16 28058001-28058100 * | GSG1L 0
## [132] 13 113397101-113397200 * | ATP11A 0
## [133] 2 8627201-8627300 * | AC011747.3 56421
## [134] 1 35382901-35383000 * | DLGAP3 0
## [135] X 27640301-27640400 * | DCAF8L2 0
## -------
## seqinfo: 25 sequences from an unspecified genome; no seqlengthsICSI fresh Vs ICSI frozen
NAME = "CBMC_intracytoplasmic_sperm_injection_fresh_embryo_vs_intracytoplasmic_sperm_injection_frozen_embryo"
samplesheet<-subset(mdc, intracytoplasmic_sperm_injection=="yes")
samplesheet$groups <- factor(samplesheet$frozen_embryo ,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr3 195420501 195420600 13.98074 -0.9538785 0.2278067 -4.187227
## chr3 195420601 195420700 13.35609 -0.8119696 0.2691280 -3.017039
## chr3 195420401 195420500 15.35303 -0.6085713 0.2028382 -3.000279
## chrUn_gl000214 63901 64000 22.02004 -0.5738894 0.2044693 -2.806727
## chr20 3077101 3077200 12.65230 0.5247725 0.2058575 2.549203
## chr16 12451901 12452000 10.33107 -0.6182277 0.2521667 -2.451663
## chr9 42416101 42416200 11.49863 -0.6769743 0.2802809 -2.415343
## chr9 44070601 44070700 28.10471 -0.5783527 0.2398014 -2.411799
## chr6 104197201 104197300 11.69186 0.5733055 0.2396681 2.392081
## chr5 754601 754700 23.74781 1.3014790 0.5521050 2.357303
## pvalue padj
## chr3 195420501 195420600 2.823828e-05 0.03591909
## chr3 195420601 195420700 2.552573e-03 0.99236223
## chr3 195420401 195420500 2.697322e-03 0.99236223
## chrUn_gl000214 63901 64000 5.004770e-03 0.99236223
## chr20 3077101 3077200 1.079695e-02 0.99236223
## chr16 12451901 12452000 1.421978e-02 0.99236223
## chr9 42416101 42416200 1.572042e-02 0.99236223
## chr9 44070601 44070700 1.587403e-02 0.99236223
## chr6 104197201 104197300 1.675315e-02 0.99236223
## chr5 754601 754700 1.840820e-02 0.99236223sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","icsi.fh",mygroups)
mygroups<-gsub("yes","icsi.fz",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm5",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm5",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)
ol_up## Hits object with 19 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 32885 | 1947
## [2] 2 46615 | 166057
## [3] 3 41852 | 0
## [4] 4 53616 | 0
## [5] 5 19939 | 0
## ... ... ... . ...
## [15] 15 46615 | 165957
## [16] 16 32607 | 0
## [17] 17 53616 | 0
## [18] 18 18072 | 62330
## [19] 19 17368 | 6745
## -------
## queryLength: 19 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 19 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 20 3077101-3077200 * | RN7SL555P 1947
## [2] 6 104197201-104197300 * | SNORA33 166057
## [3] 5 754601-754700 * | ZDHHC11B 0
## [4] 9 42004901-42005000 * | RP11-204M4.2 0
## [5] 16 15799501-15799600 * | MYH11 0
## ... ... ... ... . ... ...
## [15] 6 104197101-104197200 * | SNORA33 165957
## [16] 2 234756001-234756100 * | HJURP 0
## [17] 9 42005001-42005100 * | RP11-204M4.2 0
## [18] 15 47187301-47187400 * | AC087433.1 62330
## [19] 15 22729401-22729500 * | GOLGA6L1 6745
## -------
## seqinfo: 11 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000214, M, Un_gl000224
## - in 'y': 1, 10, 13, 14, 17, 18, 19, 2, 20, 21, 4, 5, 8, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 17 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 39153 | 0
## [2] 2 39153 | 0
## [3] 3 39153 | 0
## [4] 4 19852 | 0
## [5] 5 53630 | 0
## ... ... ... . ...
## [13] 13 17363 | 3147
## [14] 14 47137 | 18957
## [15] 15 11368 | 13337
## [16] 16 35621 | 5957
## [17] 17 19852 | 0
## -------
## queryLength: 17 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 17 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 3 195420501-195420600 * | LINC00969 0
## [2] 3 195420601-195420700 * | LINC00969 0
## [3] 3 195420401-195420500 * | LINC00969 0
## [4] 16 12451901-12452000 * | SNX29 0
## [5] 9 42416101-42416200 * | RP11-146D12.2 0
## ... ... ... ... . ... ...
## [13] 15 22671901-22672000 * | MIR4509-1 3147
## [14] 6 138464001-138464100 * | KIAA1244 18957
## [15] 12 12455601-12455700 * | RNU6-1295P 13337
## [16] 22 32717101-32717200 * | RP1-149A16.12 5957
## [17] 16 12452101-12452200 * | SNX29 0
## -------
## seqinfo: 13 sequences from an unspecified genome; no seqlengthsovarian stimulation vs GIFT
NAME = "CBMC_ovarian_stimulation_vs_gamete_intrafallopian_transfer"
samplesheet<-subset(mdc, ovarian_stimulation=="yes" | gamete_intrafallopian_transfer=="yes")
samplesheet$groups <- factor(samplesheet$gamete_intrafallopian_transfer,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr15 22517001 22517100 19.836003 -1.5072261 0.4686102 -3.216375
## chr15 22516801 22516900 15.616048 -1.4854422 0.5008465 -2.965863
## chr15 22457901 22458000 11.264676 -1.6656359 0.6003636 -2.774379
## chrX 56795901 56796000 10.770330 -1.7445134 0.6623537 -2.633809
## chrX 56802601 56802700 12.438709 -1.6276618 0.6211741 -2.620299
## chr17 1022101 1022200 10.649288 -1.8671760 0.7157074 -2.608854
## chr2 209767401 209767500 12.053792 -1.2696311 0.4900442 -2.590850
## chr11 121887901 121888000 11.234663 -2.5346499 0.9813459 -2.582830
## chr5 771901 772000 15.126876 1.1039805 0.4422674 2.496183
## chr2 59050301 59050400 9.703602 0.8342431 0.3384117 2.465172
## pvalue padj
## chr15 22517001 22517100 0.001298211 0.99119
## chr15 22516801 22516900 0.003018346 0.99119
## chr15 22457901 22458000 0.005530723 0.99119
## chrX 56795901 56796000 0.008443291 0.99119
## chrX 56802601 56802700 0.008785279 0.99119
## chr17 1022101 1022200 0.009084600 0.99119
## chr2 209767401 209767500 0.009573910 0.99119
## chr11 121887901 121888000 0.009799354 0.99119
## chr5 771901 772000 0.012553767 0.99119
## chr2 59050301 59050400 0.013694750 0.99119sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue",pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
confects <- deseq2_confects(res)
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","os",mygroups)
mygroups<-gsub("yes","gift",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm6",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm6",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000219
## - in 'y': 11, 13, 14, 18, 20, 21, 22, 3, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 19 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 41851 | 0
## [2] 2 29618 | 0
## [3] 3 28947 | 0
## [4] 4 782 | 7917
## [5] 5 46046 | 764
## ... ... ... . ...
## [15] 15 5610 | 0
## [16] 16 47711 | 0
## [17] 17 53426 | 0
## [18] 18 17552 | 2970
## [19] 19 22550 | 0
## -------
## queryLength: 19 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 19 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 5 771901-772000 * | ZDHHC11 0
## [2] 2 59050301-59050400 * | LINC01122 0
## [3] 2 16137301-16137400 * | AC010145.4 0
## [4] 1 24546101-24546200 * | RP11-10N16.2 7917
## [5] 6 52056201-52056300 * | IL17A 764
## ... ... ... ... . ... ...
## [15] 10 12404701-12404800 * | CAMK1D 0
## [16] 7 4118301-4118400 * | SDK1 0
## [17] 9 35059701-35059800 * | VCP 0
## [18] 15 28674701-28674800 * | MIR4509-3 2970
## [19] 17 21072101-21072200 * | DHRS7B 0
## -------
## seqinfo: 15 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': M
## - in 'y': 1, 10, 13, 14, 16, 18, 19, 20, 21, 22, 3, 5, 6, 7, 9, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 23 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 17359 | 3720
## [2] 2 17359 | 3520
## [3] 3 17354 | 0
## [4] 4 62108 | 0
## [5] 5 62108 | 0
## ... ... ... . ...
## [19] 19 21747 | 0
## [20] 20 62108 | 0
## [21] 21 62108 | 0
## [22] 22 51752 | 55969
## [23] 23 11497 | 34153
## -------
## queryLength: 23 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 23 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 15 22517001-22517100 * | MIR1268A 3720
## [2] 15 22516801-22516900 * | MIR1268A 3520
## [3] 15 22457901-22458000 * | AC010760.1 0
## [4] X 56795901-56796000 * | RP11-622K12.1 0
## [5] X 56802601-56802700 * | RP11-622K12.1 0
## ... ... ... ... . ... ...
## [19] 17 1022201-1022300 * | ABR 0
## [20] X 56802401-56802500 * | RP11-622K12.1 0
## [21] X 56803601-56803700 * | RP11-622K12.1 0
## [22] 8 70281201-70281300 * | RP11-744J10.3 55969
## [23] 12 20316901-20317000 * | CTC-465D4.1 34153
## -------
## seqinfo: 9 sequences from an unspecified genome; no seqlengthsovarian stimulation vs ICSI fresh
NAME = "CBMC_ovarian_stimulation_vs_intracytoplasmic_sperm_injection_fresh_embryo"
samplesheet<-subset(mdc, ovarian_stimulation=="yes" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chrUn_gl000224 122201 122300 10.557247 -0.9367754 0.2330955 -4.018849
## chrUn_gl000224 122101 122200 9.860153 -0.9160608 0.2347230 -3.902732
## chrUn_gl000219 46801 46900 22.540470 -0.4425577 0.1233755 -3.587080
## chrUn_gl000219 69701 69800 11.086934 -0.6714399 0.2259601 -2.971498
## chrUn_gl000224 96501 96600 34.499130 -0.6074352 0.2184618 -2.780510
## chrUn_gl000224 172901 173000 14.072586 -0.6593878 0.2429459 -2.714134
## chr11 91819701 91819800 13.211128 -0.4176278 0.1547081 -2.699456
## chr15 45983901 45984000 9.656976 -0.4715065 0.1768910 -2.665520
## chr8 14280901 14281000 13.421089 -0.4006120 0.1507596 -2.657291
## chrUn_gl000224 172301 172400 23.627497 -0.6112723 0.2351928 -2.599026
## pvalue padj
## chrUn_gl000224 122201 122300 5.848318e-05 0.05854211
## chrUn_gl000224 122101 122200 9.511310e-05 0.05854211
## chrUn_gl000219 46801 46900 3.344013e-04 0.13721598
## chrUn_gl000219 69701 69800 2.963507e-03 0.91201941
## chrUn_gl000224 96501 96600 5.427353e-03 0.99810330
## chrUn_gl000224 172901 173000 6.644918e-03 0.99810330
## chr11 91819701 91819800 6.945302e-03 0.99810330
## chr15 45983901 45984000 7.686947e-03 0.99810330
## chr8 14280901 14281000 7.877152e-03 0.99810330
## chrUn_gl000224 172301 172400 9.348864e-03 0.99810330sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","os",mygroups)
mygroups<-gsub("yes","icsi.fh",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm7",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm7",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000224
## - in 'y': 10, 11, 14, 15, 16, 17, 18, 19, 20, 4, 9, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 16 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 35058 | 4705
## [2] 2 28875 | 0
## [3] 3 63283 | 0
## [4] 4 51017 | 0
## [5] 5 41851 | 0
## ... ... ... . ...
## [12] 12 43839 | 0
## [13] 13 13600 | 0
## [14] 14 47711 | 0
## [15] 15 50555 | 108791
## [16] 16 39129 | 0
## -------
## queryLength: 16 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 16 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 22 20361401-20361500 * | GGTLC3 4705
## [2] 2 10767601-10767700 * | NOL10 0
## [3] X 140956801-140956900 * | MAGEC3 0
## [4] 8 23421301-23421400 * | SLC25A37 0
## [5] 5 771901-772000 * | ZDHHC11 0
## ... ... ... ... . ... ...
## [12] 5 138660801-138660900 * | MATR3 0
## [13] 12 125021101-125021200 * | NCOR2 0
## [14] 7 4118401-4118500 * | SDK1 0
## [15] 8 2242101-2242200 * | AC133633.2 108791
## [16] 3 194501101-194501200 * | AC090505.6 0
## -------
## seqinfo: 13 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000224, Un_gl000219
## - in 'y': 1, 12, 14, 16, 17, 18, 19, 20, 21, 22, 3, 5, 7, 9, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 12 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 10095 | 74471
## [2] 2 18063 | 408
## [3] 3 50861 | 0
## [4] 4 46615 | 166057
## [5] 5 10095 | 74371
## ... ... ... . ...
## [8] 8 28749 | 0
## [9] 9 7501 | 8817
## [10] 10 41242 | 5377
## [11] 11 14843 | 0
## [12] 12 28749 | 0
## -------
## queryLength: 12 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 12 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 11 91819701-91819800 * | RPL7AP57 74471
## [2] 15 45983901-45984000 * | SQRDL 408
## [3] 8 14280901-14281000 * | LRG_208 0
## [4] 6 104197201-104197300 * | SNORA33 166057
## [5] 11 91819801-91819900 * | RPL7AP57 74371
## ... ... ... ... . ... ...
## [8] 2 3082101-3082200 * | AC019118.2 0
## [9] 10 123486901-123487000 * | RP11-78A18.2 8817
## [10] 4 147448501-147448600 * | SLC10A7 5377
## [11] 13 101727001-101727100 * | NALCN 0
## [12] 2 3082201-3082300 * | AC019118.2 0
## -------
## seqinfo: 10 sequences from an unspecified genome; no seqlengthsovarian stimulation vs ICSI frozen
NAME = "CBMC_ovarian_stimulation_vs_intracytoplasmic_sperm_injection_frozen_embryo"
samplesheet<-subset(mdc, ovarian_stimulation=="yes" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chrUn_gl000224 122201 122300 10.491565 -0.9596579 0.2363674 -4.060027
## chrUn_gl000224 122101 122200 9.776760 -0.9458815 0.2372979 -3.986050
## chrUn_gl000219 46801 46900 22.586278 -0.4419041 0.1250299 -3.534388
## chrUn_gl000224 96501 96600 34.081549 -0.6516785 0.2163350 -3.012358
## chrUn_gl000219 69701 69800 11.032325 -0.6843652 0.2293089 -2.984468
## chr15 45983901 45984000 9.614643 -0.4905039 0.1784562 -2.748596
## chrUn_gl000224 172901 173000 14.004084 -0.6739810 0.2462707 -2.736748
## chr11 91819701 91819800 13.264045 -0.4205049 0.1556997 -2.700743
## chrUn_gl000219 3301 3400 14.622256 -0.6318173 0.2359205 -2.678094
## chrUn_gl000224 96401 96500 30.338126 -0.5997649 0.2258659 -2.655403
## pvalue padj
## chrUn_gl000224 122201 122300 4.906704e-05 0.04121632
## chrUn_gl000224 122101 122200 6.718227e-05 0.04121632
## chrUn_gl000219 46801 46900 4.087207e-04 0.16716677
## chrUn_gl000224 96501 96600 2.592267e-03 0.69711224
## chrUn_gl000219 69701 69800 2.840718e-03 0.69711224
## chr15 45983901 45984000 5.985118e-03 0.77071254
## chrUn_gl000224 172901 173000 6.204974e-03 0.77071254
## chr11 91819701 91819800 6.918485e-03 0.77071254
## chrUn_gl000219 3301 3400 7.404250e-03 0.77071254
## chrUn_gl000224 96401 96500 7.921373e-03 0.77071254sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","os",mygroups)
mygroups<-gsub("yes","icsi.fz",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm8",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm8",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000224
## - in 'y': 10, 11, 14, 15, 16, 17, 18, 19, 20, 3, 4, 9, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 17 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 28875 | 0
## [2] 2 31333 | 16431
## [3] 3 35058 | 4705
## [4] 4 63283 | 0
## [5] 5 51017 | 0
## ... ... ... . ...
## [13] 13 43839 | 0
## [14] 14 50555 | 108791
## [15] 15 50560 | 0
## [16] 16 13600 | 0
## [17] 17 47711 | 0
## -------
## queryLength: 17 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 17 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 2 10767601-10767700 * | NOL10 0
## [2] 2 157888201-157888300 * | AC108057.1 16431
## [3] 22 20361401-20361500 * | GGTLC3 4705
## [4] X 140956801-140956900 * | MAGEC3 0
## [5] 8 23421301-23421400 * | SLC25A37 0
## ... ... ... ... . ... ...
## [13] 5 138660801-138660900 * | MATR3 0
## [14] 8 2242101-2242200 * | AC133633.2 108791
## [15] 8 3569501-3569600 * | CSMD1 0
## [16] 12 125021101-125021200 * | NCOR2 0
## [17] 7 4118401-4118500 * | SDK1 0
## -------
## seqinfo: 12 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000224, Un_gl000219
## - in 'y': 1, 12, 14, 16, 17, 18, 19, 20, 21, 22, 3, 5, 7, 9, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 12 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 18063 | 408
## [2] 2 10095 | 74471
## [3] 3 50861 | 0
## [4] 4 10095 | 74371
## [5] 5 46615 | 166057
## ... ... ... . ...
## [8] 8 46615 | 165957
## [9] 9 28754 | 0
## [10] 10 28749 | 0
## [11] 11 41242 | 5377
## [12] 12 14843 | 0
## -------
## queryLength: 12 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 12 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 15 45983901-45984000 * | SQRDL 408
## [2] 11 91819701-91819800 * | RPL7AP57 74471
## [3] 8 14280901-14281000 * | LRG_208 0
## [4] 11 91819801-91819900 * | RPL7AP57 74371
## [5] 6 104197201-104197300 * | SNORA33 166057
## ... ... ... ... . ... ...
## [8] 6 104197101-104197200 * | SNORA33 165957
## [9] 2 3258201-3258300 * | TSSC1 0
## [10] 2 3082101-3082200 * | AC019118.2 0
## [11] 4 147448501-147448600 * | SLC10A7 5377
## [12] 13 101727001-101727100 * | NALCN 0
## -------
## seqinfo: 10 sequences from an unspecified genome; no seqlengthsGIFT vs ICSI fresh
NAME = "CBMC_gamete_intrafallopian_transfer_vs_intracytoplasmic_sperm_injection_fresh_embryo"
samplesheet<-subset(mdc, gamete_intrafallopian_transfer=="yes" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr5 180085501 180085600 16.60962 -1.653730 0.4510497 -3.666403
## chr5 180085301 180085400 16.38130 -1.344157 0.3699535 -3.633314
## chr5 180085401 180085500 18.80753 -1.497292 0.4207640 -3.558508
## chr15 22517001 22517100 18.95745 1.581406 0.4902676 3.225597
## chr5 180085201 180085300 12.10385 -1.197424 0.3778276 -3.169235
## chr15 22516801 22516900 15.09151 1.637913 0.5225503 3.134460
## chrX 56795901 56796000 11.08498 1.823778 0.5958015 3.061050
## chrX 56802601 56802700 12.71956 1.611248 0.5479423 2.940543
## chrX 56803601 56803700 15.46328 1.206362 0.4399174 2.742247
## chrUn_gl000218 19001 19100 10.64531 1.860957 0.6805514 2.734484
## pvalue padj
## chr5 180085501 180085600 0.0002459859 0.1685816
## chr5 180085301 180085400 0.0002798045 0.1685816
## chr5 180085401 180085500 0.0003729681 0.1685816
## chr15 22517001 22517100 0.0012571005 0.3891065
## chr5 180085201 180085300 0.0015284095 0.3891065
## chr15 22516801 22516900 0.0017217101 0.3891065
## chrX 56795901 56796000 0.0022056203 0.4272602
## chrX 56802601 56802700 0.0032763746 0.5553455
## chrX 56803601 56803700 0.0061020353 0.8472028
## chrUn_gl000218 19001 19100 0.0062478086 0.8472028sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","gift",mygroups)
mygroups<-gsub("yes","icsi.fh",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm9",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm9",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000218, M
## - in 'y': 1, 10, 11, 12, 14, 16, 17, 18, 19, 2, 20, 21, 22, 4, 5, 7, 9, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 17 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 17359 | 3720
## [2] 2 17359 | 3520
## [3] 3 62108 | 0
## [4] 4 62108 | 0
## [5] 5 62108 | 0
## ... ... ... . ...
## [13] 13 17359 | 9820
## [14] 14 47622 | 3124
## [15] 15 14877 | 159377
## [16] 16 39153 | 0
## [17] 17 52489 | 21610
## -------
## queryLength: 17 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 17 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 15 22517001-22517100 * | MIR1268A 3720
## [2] 15 22516801-22516900 * | MIR1268A 3520
## [3] X 56795901-56796000 * | RP11-622K12.1 0
## [4] X 56802601-56802700 * | RP11-622K12.1 0
## [5] X 56803601-56803700 * | RP11-622K12.1 0
## ... ... ... ... . ... ...
## [13] 15 22523101-22523200 * | MIR1268A 9820
## [14] 6 171040701-171040800 * | XX-C2158C12.2 3124
## [15] 13 105307201-105307300 * | RPL7P45 159377
## [16] 3 195420601-195420700 * | LINC00969 0
## [17] 8 123462401-123462500 * | RP11-94A24.1 21610
## -------
## seqinfo: 8 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000219
## - in 'y': 1, 10, 11, 12, 13, 15, 19, 20, 21, 22, 3, 4, 8, 9, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 17 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 44657 | 8876
## [2] 2 44657 | 8676
## [3] 3 44657 | 8776
## [4] 4 44657 | 8576
## [5] 5 25758 | 6997
## ... ... ... . ...
## [13] 13 41851 | 0
## [14] 14 16367 | 1430
## [15] 15 29618 | 0
## [16] 16 47809 | 3265
## [17] 17 21486 | 0
## -------
## queryLength: 17 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 17 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 5 180085501-180085600 * | FLT4 8876
## [2] 5 180085301-180085400 * | FLT4 8676
## [3] 5 180085401-180085500 * | FLT4 8776
## [4] 5 180085201-180085300 * | FLT4 8576
## [5] 18 77379301-77379400 * | RP11-567M16.2 6997
## ... ... ... ... . ... ...
## [13] 5 771901-772000 * | ZDHHC11 0
## [14] 14 76039701-76039800 * | AC007182.6 1430
## [15] 2 59050301-59050400 * | LINC01122 0
## [16] 7 7111801-7111900 * | AC092104.4 3265
## [17] 16 83260701-83260800 * | CDH13 0
## -------
## seqinfo: 10 sequences from an unspecified genome; no seqlengthsGIFT vs ICSI frozen
NAME = "CBMC_gamete_intrafallopian_transfer_vs_intracytoplasmic_sperm_injection_frozen_embryo"
samplesheet<-subset(mdc, gamete_intrafallopian_transfer=="yes" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chrM 14901 15000 85.40041 1.1496511 0.3181672 3.613355
## chr11 121887901 121888000 11.54641 2.9141594 0.8438210 3.453528
## chrM 9901 10000 28.68350 1.2758382 0.3813172 3.345871
## chrM 10501 10600 93.49152 0.9004232 0.2723920 3.305616
## chrM 9801 9900 30.37192 1.1998185 0.3851548 3.115159
## chr15 22457901 22458000 11.36440 2.0659323 0.6884990 3.000632
## chrM 9701 9800 33.16048 1.1498672 0.3896752 2.950835
## chrM 10601 10700 146.18615 0.6872482 0.2360390 2.911588
## chrM 3201 3300 69.61076 0.8435279 0.2912254 2.896478
## chrM 201 300 125.10452 0.7347937 0.2545862 2.886228
## pvalue padj
## chrM 14901 15000 0.0003022609 0.03083061
## chr11 121887901 121888000 0.0005533052 NA
## chrM 9901 10000 0.0008202468 NA
## chrM 10501 10600 0.0009476797 0.04833167
## chrM 9801 9900 0.0018384600 0.05019786
## chr15 22457901 22458000 0.0026941974 NA
## chrM 9701 9800 0.0031691602 0.05019786
## chrM 10601 10700 0.0035959714 0.05019786
## chrM 3201 3300 0.0037737751 0.05019786
## chrM 201 300 0.0038988974 0.05019786sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","gift",mygroups)
mygroups<-gsub("yes","icsi.fz",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm10",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm10",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': M
## - in 'y': 1, 10, 12, 13, 14, 16, 18, 19, 20, 21, 22, 3, 5, 7, 8, 9, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 14 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 10659 | 11062
## [2] 2 17354 | 0
## [3] 3 17359 | 3720
## [4] 4 39983 | 45865
## [5] 5 17359 | 3520
## ... ... ... . ...
## [10] 10 47622 | 2924
## [11] 11 24156 | 0
## [12] 12 21747 | 0
## [13] 13 62108 | 0
## [14] 14 29066 | 2282
## -------
## queryLength: 14 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 14 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 11 121887901-121888000 * | RP11-166D19.1 11062
## [2] 15 22457901-22458000 * | AC010760.1 0
## [3] 15 22517001-22517100 * | MIR1268A 3720
## [4] 4 52663201-52663300 * | DCUN1D4 45865
## [5] 15 22516801-22516900 * | MIR1268A 3520
## ... ... ... ... . ... ...
## [10] 6 171040501-171040600 * | XX-C2158C12.2 2924
## [11] 17 66261301-66261400 * | ARSG 0
## [12] 17 1022201-1022300 * | ABR 0
## [13] X 56802501-56802600 * | RP11-622K12.1 0
## [14] 2 24712401-24712500 * | NCOA1 2282
## -------
## seqinfo: 8 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000219
## - in 'y': 1, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 3, 4, 6, 7, 9, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 5 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 41851 | 0
## [2] 2 52744 | 0
## [3] 3 28947 | 0
## [4] 4 61198 | 1414
## [5] 5 62280 | 0
## -------
## queryLength: 5 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 5 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 5 771901-772000 * | ZDHHC11 0
## [2] 8 143323601-143323700 * | TSNARE1 0
## [3] 2 16137301-16137400 * | AC010145.4 0
## [4] X 284001-284100 * | LINC00685 1414
## [5] X 71181401-71181500 * | NHSL2 0
## -------
## seqinfo: 5 sequences from an unspecified genome; no seqlengthsWMBC
counts<-read.table("~/mr.edgeR.w.test.tsv.gz",sep="\t",header=TRUE,row.names=1)
mycol<-max(grep(".bam.counts",colnames(counts)))
counts<-counts[,1:mycol]
rownames(counts)<-paste(counts$chr,counts$start, counts$stop)
counts[,1:4]=NULL
colnames(counts)<-gsub(".bam.counts","",colnames(counts))Natural Vs Ovarian stimulation
NAME = "WCB_natural_vs_ovarian_stimulation"
samplesheet<-subset(mdw, medical_help_to_conceive=="no" | ovarian_stimulation=="yes")
samplesheet$groups <- factor(samplesheet$ovarian_stimulation,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dim(y)## [1] 4108 90dim(samplesheet)## [1] 90 19colnames(y)## [1] "W1001" "W1002" "W1003" "W1004" "W1005" "W1006" "W1007" "W1008" "W1009"
## [10] "W1010" "W1021" "W1022" "W1023" "W1024" "W1025" "W1026" "W1027" "W1028"
## [19] "W1029" "W1030" "W1041" "W1042" "W1043" "W1044" "W1046" "W1047" "W1048"
## [28] "W1049" "W1050" "W1061" "W1062" "W1064" "W1065" "W1066" "W1067" "W1068"
## [37] "W1069" "W1070" "W1081" "W1082" "W1083" "W1084" "W1085" "W1086" "W1087"
## [46] "W1088" "W1089" "W1090" "W2001" "W2002" "W2003" "W2004" "W2005" "W2006"
## [55] "W2007" "W2009" "W2010" "W2021" "W2023" "W2024" "W2025" "W2026" "W2027"
## [64] "W2028" "W2029" "W2030" "W2042" "W2043" "W2046" "W2047" "W2048" "W2049"
## [73] "W2050" "W2051" "W2052" "W2053" "W2054" "W2055" "W2057" "W2058" "W2059"
## [82] "W2060" "W3031" "W3032" "W3033" "W3034" "W3035" "W3036" "W3037" "W3038"rownames(samplesheet)[which(!rownames(samplesheet)%in%colnames(y))]## character(0)dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr22 18235701 18235800 12.00608 -0.6233634 0.10407128 -5.989774
## chr22 18235601 18235700 10.96159 -0.5996109 0.10875599 -5.513359
## chr22 47374601 47374700 10.36687 -0.5790462 0.10907366 -5.308763
## chr2 127539601 127539700 12.98883 -0.4889375 0.09263947 -5.277852
## chr21 47679401 47679500 9.72660 -0.5877305 0.11175865 -5.258926
## chr9 130947601 130947700 14.63778 -0.5283858 0.10128306 -5.216921
## chr12 6155701 6155800 12.46589 -0.6123979 0.11822069 -5.180124
## chr15 42251201 42251300 16.42990 -0.4280639 0.08299098 -5.157958
## chr20 44494301 44494400 11.02396 -0.5246181 0.10173111 -5.156909
## chr19 45812201 45812300 10.46110 -0.5815381 0.11290401 -5.150730
## pvalue padj
## chr22 18235701 18235800 2.101335e-09 8.632283e-06
## chr22 18235601 18235700 3.520483e-08 7.231071e-05
## chr22 47374601 47374700 1.103717e-07 9.257390e-05
## chr2 127539601 127539700 1.307067e-07 9.257390e-05
## chr21 47679401 47679500 1.448994e-07 9.257390e-05
## chr9 130947601 130947700 1.819214e-07 9.257390e-05
## chr12 6155701 6155800 2.217382e-07 9.257390e-05
## chr15 42251201 42251300 2.496583e-07 9.257390e-05
## chr20 44494301 44494400 2.510596e-07 9.257390e-05
## chr19 45812201 45812300 2.594741e-07 9.257390e-05sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","os",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dmw1",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dmw1",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000224, Un_gl000219, Un_gl000214, Un_gl000234, Un_gl000221
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 948 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 50305 | 4646
## [2] 2 50558 | 0
## [3] 3 21913 | 1391
## [4] 4 50558 | 90
## [5] 5 41643 | 66790
## ... ... ... . ...
## [944] 944 36047 | 0
## [945] 945 29932 | 0
## [946] 946 21486 | 0
## [947] 947 47173 | 45953
## [948] 948 4139 | 0
## -------
## queryLength: 948 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 948 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 7 148676101-148676200 * | RNY3 4646
## [2] 8 2523501-2523600 * | RP11-134O21.1 0
## [3] 17 4834101-4834200 * | GP1BA 1391
## [4] 8 2523401-2523500 * | RP11-134O21.1 90
## [5] 4 179923701-179923800 * | RP11-296L20.1 66790
## ... ... ... ... . ... ...
## [944] 22 47109601-47109700 * | CERK 0
## [945] 2 74285301-74285400 * | TET3 0
## [946] 16 83819801-83819900 * | CDH13 0
## [947] 6 141290001-141290100 * | RP11-471B18.1 45953
## [948] 1 183028901-183029000 * | LAMC1 0
## -------
## seqinfo: 28 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000225, 4_gl000194_random, Un_gl000232, Un_gl000224, 7_gl000195_random, Un_gl000231, Un_gl000237, Un_gl000222
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 1498 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 34961 | 0
## [2] 2 34961 | 0
## [3] 3 36049 | 0
## [4] 4 30899 | 79082
## [5] 5 34847 | 0
## ... ... ... . ...
## [1494] 1494 50554 | 108225
## [1495] 1495 183 | 0
## [1496] 1496 183 | 0
## [1497] 1497 183 | 0
## [1498] 1498 183 | 0
## -------
## queryLength: 1498 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 1498 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 22 18235701-18235800 * | BID 0
## [2] 22 18235601-18235700 * | BID 0
## [3] 22 47374601-47374700 * | TBC1D22A 0
## [4] 2 127539601-127539700 * | RNU6-675P 79082
## [5] 21 47679401-47679500 * | MCM3AP 0
## ... ... ... ... . ... ...
## [1494] 8 2221701-2221800 * | MYOM2 108225
## [1495] 1 2602001-2602100 * | TTC34 0
## [1496] 1 2605501-2605600 * | TTC34 0
## [1497] 1 2595601-2595700 * | TTC34 0
## [1498] 1 2605601-2605700 * | TTC34 0
## -------
## seqinfo: 32 sequences from an unspecified genome; no seqlengthsNatural Vs GIFT
NAME = "WBC_natural_vs_gamete_intrafallopian_transfer"
samplesheet<-subset(mdw, medical_help_to_conceive=="no" | gamete_intrafallopian_transfer=="yes")
samplesheet$groups <- factor(samplesheet$gamete_intrafallopian_transfer,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr17 30853101 30853200 13.22975 1.1183982 0.2491031 4.489700
## chr22 39513801 39513900 12.20109 1.1663198 0.2698930 4.321415
## chr16 81857701 81857800 10.88815 1.1653577 0.2730882 4.267330
## chr22 42088101 42088200 16.67260 0.9505196 0.2306662 4.120758
## chr2 87592701 87592800 14.67661 1.0874182 0.2702461 4.023807
## chr19 36146601 36146700 15.90479 0.9714860 0.2453981 3.958817
## chr15 59042801 59042900 12.53708 1.0339995 0.2614560 3.954775
## chr6 42166101 42166200 14.90020 1.0262018 0.2621842 3.914049
## chr13 39579601 39579700 11.24815 1.1615895 0.3035079 3.827214
## chr7 100350401 100350500 26.34002 0.9250375 0.2463861 3.754422
## pvalue padj
## chr17 30853101 30853200 7.132360e-06 0.02377211
## chr22 39513801 39513900 1.550320e-05 0.02377211
## chr16 81857701 81857800 1.978262e-05 0.02377211
## chr22 42088101 42088200 3.776273e-05 0.03403366
## chr2 87592701 87592800 5.726486e-05 0.03945240
## chr19 36146601 36146700 7.532200e-05 0.03945240
## chr15 59042801 59042900 7.660661e-05 0.03945240
## chr6 42166101 42166200 9.076136e-05 0.04089934
## chr13 39579601 39579700 1.296019e-04 0.05191278
## chr7 100350401 100350500 1.737418e-04 0.06263391sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","gift",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dmw2",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dmw2",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000217, Un_gl000214, Un_gl000239, Un_gl000219, 1_gl000192_random, Un_gl000232, Un_gl000224
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 373 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 22904 | 0
## [2] 2 35821 | 2271
## [3] 3 21471 | 0
## [4] 4 35902 | 0
## [5] 5 30161 | 3555
## ... ... ... . ...
## [369] 369 4384 | 0
## [370] 370 41127 | 0
## [371] 371 34826 | 0
## [372] 372 8834 | 7031
## [373] 373 32642 | 23994
## -------
## queryLength: 373 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 373 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 17 30853101-30853200 * | RP11-466A19.3 0
## [2] 22 39513801-39513900 * | CBX7 2271
## [3] 16 81857701-81857800 * | LRG_376 0
## [4] 22 42088101-42088200 * | C22orf46 0
## [5] 2 87592701-87592800 * | AC068279.1 3555
## ... ... ... ... . ... ...
## [369] 1 201884301-201884400 * | LMOD1 0
## [370] 4 140079701-140079800 * | ELF2 0
## [371] 21 47268101-47268200 * | PCBP3 0
## [372] 11 50220201-50220300 * | RP11-347H15.2 7031
## [373] 2 237687001-237687100 * | AC011286.1 23994
## -------
## seqinfo: 30 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000222, Un_gl000225
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 214 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 44872 | 0
## [2] 2 7562 | 0
## [3] 3 10994 | 0
## [4] 4 38610 | 0
## [5] 5 52670 | 19140
## ... ... ... . ...
## [210] 210 53230 | 83198
## [211] 211 33887 | 0
## [212] 212 7596 | 28265
## [213] 213 46533 | 54
## [214] 214 32759 | 17117
## -------
## queryLength: 214 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 214 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 6 9706701-9706800 * | OFCC1 0
## [2] 10 126694101-126694200 * | CTBP2 0
## [3] 12 2516401-2516500 * | LRG_334 0
## [4] 3 160863401-160863500 * | NMD3 0
## [5] 8 134637601-134637700 * | SNORA40 19140
## ... ... ... ... . ... ...
## [210] 9 22562901-22563000 * | RP11-399D6.2 83198
## [211] 20 54056101-54056200 * | RP5-1010E17.1 0
## [212] 10 129379201-129379300 * | NPS 28265
## [213] 6 92526201-92526300 * | RP11-40G16.1 54
## [214] 2 242466601-242466700 * | BOK-AS1 17117
## -------
## seqinfo: 25 sequences from an unspecified genome; no seqlengthsNatural Vs ICSI fresh
NAME = "WCB_natural_vs_intracytoplasmic_sperm_injection_fresh_embryo"
samplesheet<-subset(mdw, medical_help_to_conceive=="no" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr8 78869401 78869500 13.179944 -0.8471167 0.1696395 -4.993628
## chrX 135606501 135606600 12.885803 -0.7877829 0.1635452 -4.816911
## chr7 98335401 98335500 13.854443 0.5724927 0.1199350 4.773360
## chr2 10578701 10578800 10.494121 0.6409630 0.1349830 4.748472
## chr22 18235701 18235800 12.620603 -0.7487072 0.1599923 -4.679645
## chr8 78869301 78869400 10.249391 -0.8413987 0.1813358 -4.640003
## chr18 21749701 21749800 12.871286 0.6268482 0.1371546 4.570377
## chr13 114887201 114887300 10.906562 -0.8023554 0.1764635 -4.546862
## chr11 111054201 111054300 9.970166 0.6464340 0.1427947 4.527017
## chr3 195352701 195352800 9.590132 0.6430379 0.1436293 4.477066
## pvalue padj
## chr8 78869401 78869500 5.925533e-07 0.002259678
## chrX 135606501 135606600 1.457975e-06 0.002259678
## chr7 98335401 98335500 1.811775e-06 0.002259678
## chr2 10578701 10578800 2.049595e-06 0.002259678
## chr22 18235701 18235800 2.873726e-06 0.002534626
## chr8 78869301 78869400 3.484039e-06 0.002560769
## chr18 21749701 21749800 4.868479e-06 0.002931289
## chr13 114887201 114887300 5.445166e-06 0.002931289
## chr11 111054201 111054300 5.982223e-06 0.002931289
## chr3 195352701 195352800 7.567572e-06 0.003337299sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","icsi.fh",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dmw3",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dmw3",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000220, Un_gl000217, Un_gl000219, Un_gl000234, Un_gl000224, Un_gl000221, Un_gl000239, 17_gl000205_random, Un_gl000214, 1_gl000192_random
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 882 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 49302 | 56840
## [2] 2 28871 | 1293
## [3] 3 25091 | 0
## [4] 4 10367 | 72406
## [5] 5 39149 | 5596
## ... ... ... . ...
## [878] 878 26292 | 6668
## [879] 879 50872 | 0
## [880] 880 43527 | 0
## [881] 881 54702 | 8791
## [882] 882 4335 | 0
## -------
## queryLength: 882 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 882 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 7 98335401-98335500 * | RNU6-393P 56840
## [2] 2 10578701-10578800 * | ODC1 1293
## [3] 18 21749701-21749800 * | OSBPL1A 0
## [4] 11 111054201-111054300 * | C11orf53 72406
## [5] 3 195352701-195352800 * | AC069213.1 5596
## ... ... ... ... . ... ...
## [878] 19 9855901-9856000 * | ZNF846 6668
## [879] 8 15427201-15427300 * | TUSC3 0
## [880] 5 122179701-122179800 * | SNX24 0
## [881] 9 124556601-124556700 * | DAB2IP 8791
## [882] 1 200029301-200029400 * | NR5A2 0
## -------
## seqinfo: 33 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000237, Un_gl000225, 7_gl000195_random, Un_gl000222, Un_gl000224, 4_gl000194_random
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 1212 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 51878 | 190835
## [2] 2 63206 | 7710
## [3] 3 34961 | 0
## [4] 4 51878 | 190935
## [5] 5 14995 | 0
## ... ... ... . ...
## [1208] 1208 14371 | 0
## [1209] 1209 54273 | 0
## [1210] 1210 34686 | 0
## [1211] 1211 20536 | 2798
## [1212] 1212 34576 | 0
## -------
## queryLength: 1212 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 1212 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 8 78869401-78869500 * | RP11-91P17.1 190835
## [2] X 135606501-135606600 * | VGLL1 7710
## [3] 22 18235701-18235800 * | BID 0
## [4] 8 78869301-78869400 * | RP11-91P17.1 190935
## [5] 13 114887201-114887300 * | RASA3 0
## ... ... ... ... . ... ...
## [1208] 13 50622101-50622200 * | DLEU2 0
## [1209] 9 97539601-97539700 * | C9orf3 0
## [1210] 21 43714901-43715000 * | ABCG1 0
## [1211] 16 33376201-33376300 * | RP11-23E10.4 2798
## [1212] 21 38110501-38110600 * | SIM2 0
## -------
## seqinfo: 29 sequences from an unspecified genome; no seqlengthsNatural Vs ICSI frozen
NAME = "WCB_natural_vs_intracytoplasmic_sperm_injection_frozen_embryo"
samplesheet<-subset(mdw, medical_help_to_conceive=="no" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr18 77380701 77380800 16.912354 -1.5075560 0.2767851 -5.446667
## chr18 77380801 77380900 20.698877 -1.0859208 0.2339465 -4.641748
## chr9 130628301 130628400 16.980399 0.6081491 0.1431800 4.247445
## chr1 32445901 32446000 11.079459 0.6990676 0.1661313 4.207922
## chr4 148771801 148771900 13.332171 0.6223662 0.1564437 3.978213
## chr15 96559701 96559800 12.501215 -0.9851054 0.2490842 -3.954909
## chr4 148771901 148772000 9.975663 0.7075575 0.1789136 3.954745
## chr5 1938501 1938600 13.686331 -0.8631993 0.2216432 -3.894544
## chr12 6041001 6041100 34.318833 0.8115905 0.2086039 3.890581
## chr12 6040901 6041000 32.872149 0.7488709 0.1925731 3.888762
## pvalue padj
## chr18 77380701 77380800 5.132250e-08 0.0002268455
## chr18 77380801 77380900 3.454740e-06 0.0076349765
## chr9 130628301 130628400 2.162218e-05 0.0284791251
## chr1 32445901 32446000 2.577296e-05 0.0284791251
## chr4 148771801 148771900 6.943530e-05 0.0378028487
## chr15 96559701 96559800 7.656378e-05 0.0378028487
## chr4 148771901 148772000 7.661647e-05 0.0378028487
## chr5 1938501 1938600 9.838352e-05 0.0378028487
## chr12 6041001 6041100 1.000044e-04 0.0378028487
## chr12 6040901 6041000 1.007568e-04 0.0378028487sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","icsi.fz",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dmw4",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dmw4",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)
ol_up## Hits object with 525 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 54852 | 358
## [2] 2 1049 | 33429
## [3] 3 41259 | 0
## [4] 4 41259 | 0
## [5] 5 11072 | 0
## ... ... ... . ...
## [521] 521 48171 | 0
## [522] 522 32265 | 0
## [523] 523 31854 | 43212
## [524] 524 29059 | 0
## [525] 525 13707 | 17114
## -------
## queryLength: 525 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 525 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 9 130628301-130628400 * | AK1 358
## [2] 1 32445901-32446000 * | KHDRBS1 33429
## [3] 4 148771801-148771900 * | ARHGAP10 0
## [4] 4 148771901-148772000 * | ARHGAP10 0
## [5] 12 6041001-6041100 * | ANO2 0
## ... ... ... ... . ... ...
## [521] 7 32183401-32183500 * | PDE1C 0
## [522] 2 218377601-218377700 * | DIRC3 0
## [523] 2 194936101-194936200 * | AC068135.1 43212
## [524] 2 24421101-24421200 * | FAM228A 0
## [525] 12 131814801-131814900 * | RP13-507P19.2 17114
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000224, 7_gl000195_random, Un_gl000231, Un_gl000225, Un_gl000232, Un_gl000217, Un_gl000219
## - in 'y': GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 462 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 25758 | 8397
## [2] 2 25758 | 8497
## [3] 3 19216 | 11517
## [4] 4 41892 | 0
## [5] 5 52941 | 0
## ... ... ... . ...
## [458] 458 48780 | 0
## [459] 459 32893 | 23928
## [460] 460 33176 | 2994
## [461] 461 23930 | 0
## [462] 462 13310 | 0
## -------
## queryLength: 462 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 462 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 18 77380701-77380800 * | RP11-567M16.2 8397
## [2] 18 77380801-77380900 * | RP11-567M16.2 8497
## [3] 15 96559701-96559800 * | RP11-4G2.1 11517
## [4] 5 1938501-1938600 * | RP11-259O2.1 0
## [5] 9 2129201-2129300 * | SMARCA2 0
## ... ... ... ... . ... ...
## [458] 7 65492201-65492300 * | RP5-1132H15.3 0
## [459] 20 3412201-3412300 * | C20orf194 23928
## [460] 20 21485601-21485700 * | GSTM3P1 2994
## [461] 17 58298201-58298300 * | USP32 0
## [462] 12 113336401-113336500 * | RPH3A 0
## -------
## seqinfo: 31 sequences from an unspecified genome; no seqlengthsICSI fresh Vs ICSI frozen
NAME = "WCB_intracytoplasmic_sperm_injection_fresh_embryo_vs_intracytoplasmic_sperm_injection_frozen_embryo"
samplesheet<-subset(mdw, intracytoplasmic_sperm_injection=="yes")
samplesheet$groups <- factor(samplesheet$frozen_embryo ,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr1 17055401 17055500 55.494655 0.5291444 0.1439927 3.674799
## chrUn_gl000224 141501 141600 13.187467 -1.2710092 0.3627306 -3.504003
## chrUn_gl000224 136501 136600 11.591425 -1.2439351 0.3552201 -3.501872
## chr4 140079701 140079800 31.036529 0.6521172 0.1878481 3.471514
## chr4 162212101 162212200 12.649396 -1.3347748 0.3846272 -3.470308
## chr4 162212201 162212300 10.421576 -1.4468827 0.4249407 -3.404905
## chr5 53347501 53347600 9.728852 -1.0395377 0.3075009 -3.380601
## chr1 121434201 121434300 43.657565 -0.5850088 0.1749638 -3.343598
## chrUn_gl000217 86301 86400 13.445165 -0.9223583 0.2758936 -3.343166
## chrUn_gl000224 129701 129800 11.026800 -1.2770373 0.3834820 -3.330110
## pvalue padj
## chr1 17055401 17055500 0.0002380365 0.3826655
## chrUn_gl000224 141501 141600 0.0004583194 0.3826655
## chrUn_gl000224 136501 136600 0.0004620020 0.3826655
## chr4 140079701 140079800 0.0005175318 0.3826655
## chr4 162212101 162212200 0.0005198624 0.3826655
## chr4 162212201 162212300 0.0006618705 0.3826655
## chr5 53347501 53347600 0.0007232753 0.3826655
## chr1 121434201 121434300 0.0008269942 0.3826655
## chrUn_gl000217 86301 86400 0.0008282820 0.3826655
## chrUn_gl000224 129701 129800 0.0008681160 0.3826655sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","icsi.fh",mygroups)
mygroups<-gsub("yes","icsi.fz",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dmw5",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dmw5",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)
ol_up## Hits object with 133 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 567 | 4946
## [2] 2 41127 | 0
## [3] 3 33781 | 8096
## [4] 4 5721 | 22829
## [5] 5 1049 | 33429
## ... ... ... . ...
## [129] 129 28852 | 0
## [130] 130 37705 | 15124
## [131] 131 32637 | 0
## [132] 132 63608 | 730089
## [133] 133 27856 | 0
## -------
## queryLength: 133 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 133 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 1 17055401-17055500 * | FAM231C 4946
## [2] 4 140079701-140079800 * | ELF2 0
## [3] 20 47813001-47813100 * | STAU1 8096
## [4] 10 19206501-19206600 * | RP11-288D15.1 22829
## [5] 1 32445901-32446000 * | KHDRBS1 33429
## ... ... ... ... . ... ...
## [129] 2 9997801-9997900 * | TAF1B 0
## [130] 3 109231701-109231800 * | AC092905.1 15124
## [131] 2 237415101-237415200 * | IQCA1 0
## [132] Y 1922601-1922700 * | RNU6-1334P 730089
## [133] 19 47273401-47273500 * | FKRP 0
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000224, Un_gl000217, Un_gl000219, Un_gl000220
## - in 'y': 15, 21, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 137 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 41450 | 87904
## [2] 2 41450 | 87804
## [3] 3 42518 | 0
## [4] 4 2811 | 111878
## [5] 5 42382 | 0
## ... ... ... . ...
## [133] 133 50537 | 0
## [134] 134 39153 | 0
## [135] 135 33121 | 1813
## [136] 136 44587 | 0
## [137] 137 54120 | 35242
## -------
## queryLength: 137 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 137 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 4 162212101-162212200 * | RP11-234O6.2 87904
## [2] 4 162212201-162212300 * | RP11-234O6.2 87804
## [3] 5 53347501-53347600 * | ARL15 0
## [4] 1 121434201-121434300 * | RP11-344P13.1 111878
## [5] 5 39392601-39392700 * | DAB2 0
## ... ... ... ... . ... ...
## [133] 8 1085901-1086000 * | CTD-2281E23.2 0
## [134] 3 195425401-195425500 * | LINC00969 0
## [135] 20 18479701-18479800 * | RBBP9 1813
## [136] 5 177996301-177996400 * | COL23A1 0
## [137] 9 90076801-90076900 * | DAPK1 35242
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengthsovarian stimulation vs GIFT
NAME = "WCB_ovarian_stimulation_vs_gamete_intrafallopian_transfer"
samplesheet<-subset(mdw, ovarian_stimulation=="yes" | gamete_intrafallopian_transfer=="yes")
samplesheet$groups <- factor(samplesheet$gamete_intrafallopian_transfer,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr22 16693101 16693200 9.980976 1.1339874 0.2952526 3.840736
## chr17 30853101 30853200 14.995698 0.9878678 0.2604167 3.793412
## chr12 117110101 117110200 9.921964 1.1579951 0.3411362 3.394525
## chr7 16438401 16438500 11.458974 -2.3288024 0.6931108 -3.359928
## chr16 83819801 83819900 16.553392 0.8825193 0.2795569 3.156850
## chr17 76414601 76414700 13.378902 -1.5315581 0.4904426 -3.122808
## chr19 23506701 23506800 15.842338 0.8930220 0.2866894 3.114946
## chr9 15085901 15086000 12.815366 0.8945127 0.2895910 3.088882
## chr5 180085401 180085500 17.119114 1.7378892 0.5747113 3.023934
## chr5 180085501 180085600 15.107439 1.9483332 0.6498883 2.997951
## pvalue padj
## chr22 16693101 16693200 0.0001226660 0.2920558
## chr17 30853101 30853200 0.0001485911 0.2920558
## chr12 117110101 117110200 0.0006874785 0.7661794
## chr7 16438401 16438500 0.0007796280 0.7661794
## chr16 83819801 83819900 0.0015948332 0.8479443
## chr17 76414601 76414700 0.0017913442 0.8479443
## chr19 23506701 23506800 0.0018397862 0.8479443
## chr9 15085901 15086000 0.0020091088 0.8479443
## chr5 180085401 180085500 0.0024951078 0.8479443
## chr5 180085501 180085600 0.0027180141 0.8479443sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","os",mygroups)
mygroups<-gsub("yes","gift",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dmw6",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dmw6",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000214
## - in 'y': 3, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 84 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 34896 | 5855
## [2] 2 22904 | 0
## [3] 3 13383 | 0
## [4] 4 21486 | 0
## [5] 5 27026 | 0
## ... ... ... . ...
## [80] 80 33717 | 186
## [81] 81 34826 | 0
## [82] 82 42796 | 0
## [83] 83 41747 | 0
## [84] 84 35340 | 0
## -------
## queryLength: 84 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 84 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 22 16693101-16693200 * | LA16c-13E4.3 5855
## [2] 17 30853101-30853200 * | RP11-466A19.3 0
## [3] 12 117110101-117110200 * | RP11-497G19.1 0
## [4] 16 83819801-83819900 * | CDH13 0
## [5] 19 23506701-23506800 * | CTB-176F20.3 0
## ... ... ... ... . ... ...
## [80] 20 44604901-44605000 * | FTLP1 186
## [81] 21 47268001-47268100 * | PCBP3 0
## [82] 5 70387601-70387700 * | RP11-195E2.1 0
## [83] 4 187036301-187036400 * | FAM149A 0
## [84] 22 24234701-24234800 * | AP000350.10 0
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)
ol_down## Hits object with 60 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 47898 | 0
## [2] 2 24444 | 0
## [3] 3 5396 | 721
## [4] 4 44587 | 0
## [5] 5 24800 | 0
## ... ... ... . ...
## [56] 56 30730 | 0
## [57] 57 37635 | 0
## [58] 58 47870 | 5090
## [59] 59 45036 | 41241
## [60] 60 568 | 0
## -------
## queryLength: 60 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 60 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 7 16438401-16438500 * | ISPD 0
## [2] 17 76414601-76414700 * | PGS1 0
## [3] 1 248736201-248736300 * | OR2T34 721
## [4] 5 177996501-177996600 * | COL23A1 0
## [5] 18 7688101-7688200 * | PTPRM 0
## ... ... ... ... . ... ...
## [56] 2 113356001-113356100 * | AC012442.5 0
## [57] 3 101913201-101913300 * | ZPLD1 0
## [58] 7 12699701-12699800 * | AC011891.5 5090
## [59] 6 21313301-21313400 * | RP1-135L22.1 41241
## [60] 1 17123001-17123100 * | CROCC 0
## -------
## seqinfo: 18 sequences from an unspecified genome; no seqlengthsovarian stimulation vs ICSI fresh
NAME = "WCB_ovarian_stimulation_vs_intracytoplasmic_sperm_injection_fresh_embryo"
samplesheet<-subset(mdw, ovarian_stimulation=="yes" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr22 18137301 18137400 9.779628 0.7210510 0.1891315 3.812431
## chr20 46505901 46506000 12.066651 -0.5706467 0.1608996 -3.546600
## chr20 46506001 46506100 12.644342 -0.5613458 0.1588854 -3.533023
## chr6 5254301 5254400 16.185867 -0.5476619 0.1588228 -3.448257
## chr11 114719601 114719700 12.652462 -0.5282294 0.1603397 -3.294440
## chr1 568201 568300 54.388852 -0.3727779 0.1181326 -3.155588
## chr7 98335401 98335500 15.787574 0.4398273 0.1440408 3.053492
## chr10 53786501 53786600 10.832078 0.5772439 0.1911099 3.020482
## chr13 82443601 82443700 12.732790 -0.4574634 0.1574567 -2.905328
## chr9 4118801 4118900 12.005536 0.5094682 0.1766290 2.884398
## pvalue padj
## chr22 18137301 18137400 0.0001376064 0.5484674
## chr20 46505901 46506000 0.0003902360 0.5484674
## chr20 46506001 46506100 0.0004108370 0.5484674
## chr6 5254301 5254400 0.0005642173 0.5649225
## chr11 114719601 114719700 0.0009861790 0.7899294
## chr1 568201 568300 0.0016017491 0.9994297
## chr7 98335401 98335500 0.0022619494 0.9994297
## chr10 53786501 53786600 0.0025237290 0.9994297
## chr13 82443601 82443700 0.0036686829 0.9994297
## chr9 4118801 4118900 0.0039216271 0.9994297sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","os",mygroups)
mygroups<-gsub("yes","icsi.fh",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dmw7",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dmw7",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000225
## - in 'y': 21, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 70 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 34959 | 0
## [2] 2 49302 | 56840
## [3] 3 6329 | 0
## [4] 4 52962 | 0
## [5] 5 22912 | 0
## ... ... ... . ...
## [66] 66 6799 | 0
## [67] 67 33419 | 0
## [68] 68 32671 | 1130
## [69] 69 10492 | 178091
## [70] 70 48255 | 0
## -------
## queryLength: 70 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 70 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 22 18137301-18137400 * | BCL2L13 0
## [2] 7 98335401-98335500 * | RNU6-393P 56840
## [3] 10 53786501-53786600 * | PRKG1 0
## [4] 9 4118801-4118900 * | GLIS3 0
## [5] 17 32115801-32115900 * | ASIC2 0
## ... ... ... ... . ... ...
## [66] 10 84641801-84641900 * | NRG3 0
## [67] 20 33182601-33182700 * | PIGU 0
## [68] 2 239113501-239113600 * | ILKAP 1130
## [69] 11 116000301-116000400 * | AP000797.2 178091
## [70] 7 37484301-37484400 * | ELMO1 0
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000219, 7_gl000195_random, Un_gl000224
## - in 'y': 12, 16, 21, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 64 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 33765 | 3883
## [2] 2 33765 | 3983
## [3] 3 44812 | 0
## [4] 4 10475 | 140238
## [5] 5 38 | 0
## ... ... ... . ...
## [60] 60 45693 | 0
## [61] 61 39662 | 0
## [62] 62 1873 | 0
## [63] 63 37718 | 0
## [64] 64 26150 | 0
## -------
## queryLength: 64 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 64 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 20 46505901-46506000 * | RNA5SP486 3883
## [2] 20 46506001-46506100 * | RNA5SP486 3983
## [3] 6 5254301-5254400 * | LYRM4 0
## [4] 11 114719601-114719700 * | NXPE2 140238
## [5] 1 568201-568300 * | RP5-857K21.7 0
## ... ... ... ... . ... ...
## [60] 6 34729201-34729300 * | SNRPC 0
## [61] 4 22505901-22506000 * | GPR125 0
## [62] 1 65688901-65689000 * | AK4 0
## [63] 3 111279801-111279900 * | CD96 0
## [64] 19 6919601-6919700 * | EMR1 0
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthsovarian stimulation vs ICSI frozen
NAME = "WCB_ovarian_stimulation_vs_gamete_intrafallopian_transfer"
samplesheet<-subset(mdw, ovarian_stimulation=="yes" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr22 18137301 18137400 9.672049 0.7234614 0.1894935 3.817869
## chr20 46506001 46506100 12.737820 -0.5606210 0.1617836 -3.465253
## chr10 53786501 53786600 10.950650 0.6281321 0.1829794 3.432803
## chr20 46505901 46506000 12.181244 -0.5624272 0.1639817 -3.429817
## chr1 568201 568300 54.385106 -0.3970362 0.1209857 -3.281678
## chr6 5254301 5254400 16.457519 -0.5198147 0.1612261 -3.224135
## chr11 114719601 114719700 12.748471 -0.5275933 0.1648446 -3.200550
## chr21 41496201 41496300 9.969344 0.6933557 0.2189795 3.166304
## chr2 203793501 203793600 11.672169 0.5324327 0.1709478 3.114592
## chr12 33515801 33515900 10.072216 0.5100111 0.1759576 2.898488
## pvalue padj
## chr22 18137301 18137400 0.0001346092 0.5370906
## chr20 46506001 46506100 0.0005297330 0.6024781
## chr10 53786501 53786600 0.0005973764 0.6024781
## chr20 46505901 46506000 0.0006039881 0.6024781
## chr1 568201 568300 0.0010319121 0.7700175
## chr6 5254301 5254400 0.0012635393 0.7700175
## chr11 114719601 114719700 0.0013716580 0.7700175
## chr21 41496201 41496300 0.0015438947 0.7700175
## chr2 203793501 203793600 0.0018419929 0.8166169
## chr12 33515801 33515900 0.0037496652 0.9942509sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","os",mygroups)
mygroups<-gsub("yes","icsi.fz",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dmw8",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dmw8",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000225
## - in 'y': 15, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 76 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 34959 | 0
## [2] 2 6329 | 0
## [3] 3 34651 | 0
## [4] 4 32035 | 0
## [5] 5 11716 | 210
## ... ... ... . ...
## [72] 72 48741 | 0
## [73] 73 39565 | 0
## [74] 74 25091 | 0
## [75] 75 50846 | 15235
## [76] 76 34696 | 0
## -------
## queryLength: 76 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 76 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 22 18137301-18137400 * | BCL2L13 0
## [2] 10 53786501-53786600 * | PRKG1 0
## [3] 21 41496201-41496300 * | DSCAM 0
## [4] 2 203793501-203793600 * | CARF 0
## [5] 12 33515801-33515900 * | SNORD112 210
## ... ... ... ... . ... ...
## [72] 7 64362201-64362300 * | ZNF273 0
## [73] 4 11781301-11781400 * | RP11-281P23.2 0
## [74] 18 21749701-21749800 * | OSBPL1A 0
## [75] 8 12917201-12917300 * | RNU6-842P 15235
## [76] 21 43953501-43953600 * | SLC37A1 0
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000219, 7_gl000195_random, Un_gl000224, Un_gl000232
## - in 'y': 12, 16, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 63 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 33765 | 3983
## [2] 2 33765 | 3883
## [3] 3 38 | 0
## [4] 4 44812 | 0
## [5] 5 10475 | 140238
## ... ... ... . ...
## [59] 59 32311 | 0
## [60] 60 5508 | 21253
## [61] 61 15894 | 0
## [62] 62 29731 | 0
## [63] 63 19322 | 0
## -------
## queryLength: 63 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 63 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 20 46506001-46506100 * | RNA5SP486 3983
## [2] 20 46505901-46506000 * | RNA5SP486 3883
## [3] 1 568201-568300 * | RP5-857K21.7 0
## [4] 6 5254301-5254400 * | LYRM4 0
## [5] 11 114719601-114719700 * | NXPE2 140238
## ... ... ... ... . ... ...
## [59] 2 219783901-219784000 * | AC073128.10 0
## [60] 10 5615601-5615700 * | RP13-463N16.6 21253
## [61] 14 53764001-53764100 * | AL163953.3 0
## [62] 2 64944101-64944200 * | SERTAD2 0
## [63] 15 101771701-101771800 * | CHSY1 0
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengthsGIFT vs ICSI fresh
NAME = "WCB_gamete_intrafallopian_transfer_vs_intracytoplasmic_sperm_injection_fresh_embryo"
samplesheet<-subset(mdw, gamete_intrafallopian_transfer=="yes" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chr5 180085501 180085600 16.470312 -2.399379 0.5171319 -4.639781
## chr5 180085401 180085500 18.252841 -2.167830 0.4786014 -4.529512
## chr16 83819801 83819900 14.489352 -1.311364 0.3730819 -3.514950
## chr11 131925601 131925700 13.758961 -1.361866 0.3933131 -3.462549
## chr5 54244201 54244300 9.764983 2.871821 0.9020866 3.183531
## chr5 177996501 177996600 14.169417 1.715696 0.5609513 3.058547
## chr5 80451501 80451600 9.780591 4.447250 1.4588228 3.048519
## chr5 69301501 69301600 11.711080 -1.235108 0.4075678 -3.030436
## chr22 16693101 16693200 10.871257 -1.196010 0.3953796 -3.024967
## chr7 16438401 16438500 11.850219 2.256864 0.7481360 3.016649
## pvalue padj
## chr5 180085501 180085600 3.487788e-06 0.01235021
## chr5 180085401 180085500 5.912022e-06 0.01235021
## chr16 83819801 83819900 4.398368e-04 0.55889594
## chr11 131925601 131925700 5.350847e-04 0.55889594
## chr5 54244201 54244300 1.454904e-03 0.99972323
## chr5 177996501 177996600 2.224133e-03 0.99972323
## chr5 80451501 80451600 2.299721e-03 0.99972323
## chr5 69301501 69301600 2.442008e-03 0.99972323
## chr22 16693101 16693200 2.486600e-03 0.99972323
## chr7 16438401 16438500 2.555859e-03 0.99972323sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","gift",mygroups)
mygroups<-gsub("yes","icsi.fh",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dmw9",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dmw9",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000239
## - in 'y': 10, 13, 21, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X, Y
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_up## Hits object with 62 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 42531 | 7762
## [2] 2 44587 | 0
## [3] 3 43016 | 0
## [4] 4 47898 | 0
## [5] 5 24444 | 0
## ... ... ... . ...
## [58] 58 39150 | 260
## [59] 59 47518 | 0
## [60] 60 48859 | 0
## [61] 61 49699 | 33787
## [62] 62 28771 | 0
## -------
## queryLength: 62 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 62 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 5 54244201-54244300 * | RP11-45H22.3 7762
## [2] 5 177996501-177996600 * | COL23A1 0
## [3] 5 80451501-80451600 * | RASGRF2 0
## [4] 7 16438401-16438500 * | ISPD 0
## [5] 17 76414601-76414700 * | PGS1 0
## ... ... ... ... . ... ...
## [58] 3 195366501-195366600 * | RP11-141C7.4 260
## [59] 6 165877101-165877200 * | PDE10A 0
## [60] 7 70182301-70182400 * | AUTS2 0
## [61] 7 118625101-118625200 * | RP11-533K11.1 33787
## [62] 2 3749001-3749100 * | ALLC 0
## -------
## seqinfo: 20 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000224, Un_gl000214, Un_gl000219
## - in 'y': 20, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 74 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 44657 | 8876
## [2] 2 44657 | 8776
## [3] 3 21486 | 0
## [4] 4 10899 | 0
## [5] 5 42771 | 19473
## ... ... ... . ...
## [70] 70 35902 | 0
## [71] 71 7019 | 8260
## [72] 72 50510 | 0
## [73] 73 21438 | 0
## [74] 74 9617 | 25208
## -------
## queryLength: 74 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 74 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 5 180085501-180085600 * | FLT4 8876
## [2] 5 180085401-180085500 * | FLT4 8776
## [3] 16 83819801-83819900 * | CDH13 0
## [4] 11 131925601-131925700 * | NTM 0
## [5] 5 69301501-69301600 * | SERF1B 19473
## ... ... ... ... . ... ...
## [70] 22 42088101-42088200 * | C22orf46 0
## [71] 10 96153901-96154000 * | TBC1D12 8260
## [72] 7 158743201-158743300 * | WDR60 0
## [73] 16 80473501-80473600 * | RP11-525K10.3 0
## [74] 11 69659001-69659100 * | FGF3 25208
## -------
## seqinfo: 25 sequences from an unspecified genome; no seqlengthsGIFT vs ICSI frozen
NAME = "WCB_gamete_intrafallopian_transfer_vs_intracytoplasmic_sperm_injection_frozen_embryo"
samplesheet<-subset(mdw, gamete_intrafallopian_transfer=="yes" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]## baseMean log2FoldChange lfcSE stat
## chrY 2144901 2145000 10.676552 -2.0128241 0.6172267 -3.261078
## chr2 73946601 73946700 10.001901 2.1337142 0.7309475 2.919107
## chr22 16693101 16693200 12.265737 -1.2467815 0.4880763 -2.554481
## chr17 1062401 1062500 30.321491 -0.9421519 0.3764385 -2.502805
## chr13 110221501 110221600 9.911565 1.6834761 0.6743905 2.496293
## chr2 73946701 73946800 10.396826 1.5514510 0.6450579 2.405134
## chr2 73946801 73946900 9.557529 1.6012139 0.6706665 2.387496
## chr2 3749101 3749200 11.951058 1.4677959 0.6299287 2.330099
## chr6 24538901 24539000 10.081781 1.4884713 0.6444221 2.309777
## chr18 77380801 77380900 14.570227 -1.1827601 0.5183389 -2.281828
## pvalue padj
## chrY 2144901 2145000 0.001109896 0.9997649
## chr2 73946601 73946700 0.003510355 0.9997649
## chr22 16693101 16693200 0.010634634 0.9997649
## chr17 1062401 1062500 0.012321354 0.9997649
## chr13 110221501 110221600 0.012549896 0.9997649
## chr2 73946701 73946800 0.016166521 0.9997649
## chr2 73946801 73946900 0.016963575 0.9997649
## chr2 3749101 3749200 0.019800937 0.9997649
## chr6 24538901 24539000 0.020900504 0.9997649
## chr18 77380801 77380900 0.022499500 0.9997649sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#heatmap chart
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","gift",mygroups)
mygroups<-gsub("yes","icsi.fz",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dmw10",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dmw10",mx=y,groups=mygroups,n=15)
## Annotation
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)
ol_up## Hits object with 19 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 29920 | 9530
## [2] 2 14912 | 159028
## [3] 3 29920 | 9430
## [4] 4 29920 | 9330
## [5] 5 28771 | 0
## ... ... ... . ...
## [15] 15 14275 | 1365
## [16] 16 24444 | 0
## [17] 17 45715 | 0
## [18] 18 5135 | 0
## [19] 19 5674 | 747
## -------
## queryLength: 19 / subjectLength: 64102gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up## GRanges object with 19 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] 2 73946601-73946700 * | TPRKB 9530
## [2] 13 110221501-110221600 * | LINC00676 159028
## [3] 2 73946701-73946800 * | TPRKB 9430
## [4] 2 73946801-73946900 * | TPRKB 9330
## [5] 2 3749101-3749200 * | ALLC 0
## ... ... ... ... . ... ...
## [15] 13 45875601-45875700 * | AL138963.1 1365
## [16] 17 76414601-76414700 * | PGS1 0
## [17] 6 35546901-35547000 * | FKBP5 0
## [18] 1 236330101-236330200 * | GPR137B 0
## [19] 10 15555101-15555200 * | ITGA8 747
## -------
## seqinfo: 10 sequences from an unspecified genome; no seqlengthsgr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': Un_gl000217, Un_gl000214, Un_gl000219, Un_gl000224
## - in 'y': 10, 11, 12, 13, 14, 15, 19, 4, 6, 7, 8, GL000191.1, GL000192.1, GL000193.1, GL000194.1, GL000195.1, GL000196.1, GL000199.1, GL000201.1, GL000204.1, GL000205.1, GL000209.1, GL000211.1, GL000212.1, GL000213.1, GL000215.1, GL000216.1, GL000218.1, GL000219.1, GL000220.1, GL000221.1, GL000222.1, GL000223.1, GL000224.1, GL000225.1, GL000228.1, GL000229.1, GL000230.1, GL000231.1, GL000233.1, GL000236.1, GL000237.1, GL000240.1, GL000241.1, GL000242.1, GL000243.1, GL000247.1, HG1007_PATCH, HG1032_PATCH, HG104_HG975_PATCH, HG1063_PATCH, HG1074_PATCH, HG1079_PATCH, HG1082_HG167_PATCH, HG1091_PATCH, HG1133_PATCH, HG1146_PATCH, HG115_PATCH, HG1208_PATCH, HG1211_PATCH, HG122_PATCH, HG1257_PATCH, HG1287_PATCH, HG1292_PATCH, HG1293_PATCH, HG1304_PATCH, HG1308_PATCH, HG1322_PATCH, HG1350_HG959_PATCH, HG1423_PATCH, HG1424_PATCH, HG1425_PATCH, HG1426_PATCH, HG142_HG150_NOVEL_TEST, HG1433_PATCH, HG1434_PATCH, HG1435_PATCH, HG1436_HG1432_PATCH, HG1437_PATCH, HG1438_PATCH, HG1439_PATCH, HG1440_PATCH, HG1441_PATCH, HG1442_PATCH, HG1443_HG1444_PATCH, HG144_PATCH, HG1453_PATCH, HG1458_PATCH, HG1459_PATCH, HG1462_PATCH, HG1463_PATCH, HG1472_PATCH, HG1479_PATCH, HG1486_PATCH, HG1487_PATCH, HG1488_PATCH, HG1490_PATCH, HG1497_PATCH, HG14_PATCH, HG1500_PATCH, HG1501_PATCH, HG1502_PATCH, HG151_NOVEL_TEST, HG1591_PATCH, HG1592_PATCH, HG1595_PATCH, HG1699_PATCH, HG174_HG254_PATCH, HG183_PATCH, HG185_PATCH, HG186_PATCH, HG193_PATCH, HG19_PATCH, HG237_PATCH, HG243_PATCH, HG256_PATCH, HG271_PATCH, HG27_PATCH, HG280_PATCH, HG281_PATCH, HG299_PATCH, HG29_PATCH, HG305_PATCH, HG306_PATCH, HG311_PATCH, HG325_PATCH, HG329_PATCH, HG339_PATCH, HG344_PATCH, HG348_PATCH, HG357_PATCH, HG375_PATCH, HG385_PATCH, HG388_HG400_PATCH, HG414_PATCH, HG417_PATCH, HG418_PATCH, HG444_PATCH, HG480_HG481_PATCH, HG497_PATCH, HG506_HG507_HG1000_PATCH, HG50_PATCH, HG531_PATCH, HG536_PATCH, HG544_PATCH, HG686_PATCH, HG706_PATCH, HG729_PATCH, HG730_PATCH, HG736_PATCH, HG745_PATCH, HG747_PATCH, HG748_PATCH, HG75_PATCH, HG79_PATCH, HG7_PATCH, HG858_PATCH, HG865_PATCH, HG871_PATCH, HG873_PATCH, HG883_PATCH, HG905_PATCH, HG944_PATCH, HG946_PATCH, HG953_PATCH, HG957_PATCH, HG962_PATCH, HG971_PATCH, HG979_PATCH, HG987_PATCH, HG989_PATCH, HG990_PATCH, HG991_PATCH, HG996_PATCH, HG998_1_PATCH, HG998_2_PATCH, HG999_1_PATCH, HG999_2_PATCH, HSCHR10_1_CTG2, HSCHR10_1_CTG5, HSCHR12_1_CTG1, HSCHR12_1_CTG2_1, HSCHR12_1_CTG5, HSCHR12_2_CTG2, HSCHR12_2_CTG2_1, HSCHR12_3_CTG2_1, HSCHR15_1_CTG4, HSCHR15_1_CTG8, HSCHR16_1_CTG3_1, HSCHR16_2_CTG3_1, HSCHR17_1, HSCHR17_1_CTG1, HSCHR17_1_CTG4, HSCHR17_2_CTG4, HSCHR17_3_CTG4, HSCHR17_4_CTG4, HSCHR17_5_CTG4, HSCHR17_6_CTG4, HSCHR18_1_CTG1_1, HSCHR18_1_CTG2_1, HSCHR18_2_CTG2, HSCHR18_2_CTG2_1, HSCHR19LRC_COX1_CTG1, HSCHR19LRC_COX2_CTG1, HSCHR19LRC_LRC_I_CTG1, HSCHR19LRC_LRC_J_CTG1, HSCHR19LRC_LRC_S_CTG1, HSCHR19LRC_LRC_T_CTG1, HSCHR19LRC_PGF1_CTG1, HSCHR19LRC_PGF2_CTG1, HSCHR19_1_CTG3, HSCHR19_1_CTG3_1, HSCHR19_2_CTG3, HSCHR19_3_CTG3, HSCHR1_1_CTG31, HSCHR1_2_CTG31, HSCHR1_3_CTG31, HSCHR20_1_CTG1, HSCHR21_2_CTG1_1, HSCHR21_3_CTG1_1, HSCHR21_4_CTG1_1, HSCHR22_1_CTG1, HSCHR22_1_CTG2, HSCHR22_2_CTG1, HSCHR2_1_CTG1, HSCHR2_1_CTG12, HSCHR2_2_CTG12, HSCHR3_1_CTG1, HSCHR3_1_CTG2_1, HSCHR4_1, HSCHR4_1_CTG12, HSCHR4_1_CTG6, HSCHR4_2_CTG9, HSCHR5_1_CTG1, HSCHR5_1_CTG2, HSCHR5_1_CTG5, HSCHR5_2_CTG1, HSCHR5_3_CTG1, HSCHR6_1_CTG5, HSCHR6_MHC_APD, HSCHR6_MHC_COX, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, HSCHR6_MHC_QBL, HSCHR6_MHC_SSTO, HSCHR7_1_CTG6, HSCHR9_1_CTG1, HSCHR9_1_CTG35, HSCHR9_2_CTG35, HSCHR9_3_CTG35, LRG_123, LRG_162, LRG_183, LRG_187, LRG_239, LRG_311, LRG_415, LRG_93, MT, X
## Make sure to always combine/compare objects based on the same reference
## genome (use suppressWarnings() to suppress this warning).ol_down## Hits object with 15 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## [1] 1 63608 | 507789
## [2] 2 34896 | 5855
## [3] 3 21747 | 0
## [4] 4 25758 | 8497
## [5] 5 53674 | 719
## ... ... ... . ...
## [11] 11 33717 | 186
## [12] 12 34826 | 0
## [13] 13 21486 | 0
## [14] 14 43325 | 0
## [15] 15 21747 | 0
## -------
## queryLength: 15 / subjectLength: 64102gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down## GRanges object with 15 ranges and 2 metadata columns:
## seqnames ranges strand | gene distance
## <Rle> <IRanges> <Rle> | <character> <DataFrame>
## [1] Y 2144901-2145000 * | RNU6-1334P 507789
## [2] 22 16693101-16693200 * | LA16c-13E4.3 5855
## [3] 17 1062401-1062500 * | ABR 0
## [4] 18 77380801-77380900 * | RP11-567M16.2 8497
## [5] 9 44070601-44070700 * | CR848007.2 719
## ... ... ... ... . ... ...
## [11] 20 44604901-44605000 * | FTLP1 186
## [12] 21 47268001-47268100 * | PCBP3 0
## [13] 16 83819801-83819900 * | CDH13 0
## [14] 5 107687601-107687700 * | FBXL17 0
## [15] 17 1062301-1062400 * | ABR 0
## -------
## seqinfo: 16 sequences from an unspecified genome; no seqlengthssummary of DMRs in CBMCs
Natural Vs Ovarian stimulation DMR- 1336 hypermethylated- 521 hypomethylated- 815
Natural Vs GIFT DMR- 0 hypermethylated- 0 hypomethylated- 0
Natural Vs ICSI fresh DMR-338 hypermethylated-304 hypomethylated-34
Natural Vs ICSI frozen DMR- 261 hypermethylated- 248 hypomethylated- 13
ICSI fresh Vs frozen DMR- 1 hypermethylated- 0 hypomethylated- 0
Ovarian stimulation Vs GIFT DMR- 0 hypermethylated- 0 hypomethylated- 0
Ovarian stimulation Vs ICSI fresh DMR- 0 hypermethylated- 0 hypomethylated- 0
Ovarian stimulation Vs ICSI frozen DMR- 2 hypermethylated- 0 hypomethylated- 2
GIFT Vs ICSI fresh DMR- 0 hypermethylated- 0 hypomethylated- 0
GIFT Vs ICSI frozen DMR-2 hypermethylated-2 hypomethylated-0